# Installing the libraries with the specified version.
!pip install pandas==1.5.3 numpy==1.25.2 matplotlib==3.7.1 seaborn==0.13.1 scikit-learn==1.2.2 statsmodels==0.14.1 -q --user

# Libraries to help with reading and manipulating data
import pandas as pd
import numpy as np

# libaries to help with data visualization
import matplotlib.pyplot as plt
import seaborn as sns

# Removes the limit for the number of displayed columns
pd.set_option("display.max_columns", None)
# Sets the limit for the number of displayed rows
pd.set_option("display.max_rows", 200)
# setting the precision of floating numbers to 5 decimal points
pd.set_option("display.float_format", lambda x: "%.5f" % x)

# Library to split data
from sklearn.model_selection import train_test_split

# To build model for prediction
import statsmodels.stats.api as sms
from statsmodels.stats.outliers_influence import variance_inflation_factor
import statsmodels.api as sm
from statsmodels.tools.tools import add_constant
from sklearn.tree import DecisionTreeClassifier
from sklearn import tree

# To tune different models
from sklearn.model_selection import GridSearchCV


# To get diferent metric scores
from sklearn.metrics import (
    f1_score,
    accuracy_score,
    recall_score,
    precision_score,
    confusion_matrix,
    roc_auc_score,
    precision_recall_curve,
    roc_curve,
    make_scorer,
)

import warnings
warnings.filterwarnings("ignore")

from statsmodels.tools.sm_exceptions import ConvergenceWarning
warnings.simplefilter("ignore", ConvergenceWarning)

df= pd.read_csv('/Users/Moses Okesola/Downloads/INNHotelsGroup (1).csv')

# The first 5 rows of the data
df.head()

# The last 5 rows of the data
df.tail()

# view dimensions of the data
df.shape

(36275, 19)

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 36275 entries, 0 to 36274
Data columns (total 19 columns):
 #   Column                                Non-Null Count  Dtype  
---  ------                                --------------  -----  
 0   Booking_ID                            36275 non-null  object 
 1   no_of_adults                          36275 non-null  int64  
 2   no_of_children                        36275 non-null  int64  
 3   no_of_weekend_nights                  36275 non-null  int64  
 4   no_of_week_nights                     36275 non-null  int64  
 5   type_of_meal_plan                     36275 non-null  object 
 6   required_car_parking_space            36275 non-null  int64  
 7   room_type_reserved                    36275 non-null  object 
 8   lead_time                             36275 non-null  int64  
 9   arrival_year                          36275 non-null  int64  
 10  arrival_month                         36275 non-null  int64  
 11  arrival_date                          36275 non-null  int64  
 12  market_segment_type                   36275 non-null  object 
 13  repeated_guest                        36275 non-null  int64  
 14  no_of_previous_cancellations          36275 non-null  int64  
 15  no_of_previous_bookings_not_canceled  36275 non-null  int64  
 16  avg_price_per_room                    36275 non-null  float64
 17  no_of_special_requests                36275 non-null  int64  
 18  booking_status                        36275 non-null  object 
dtypes: float64(1), int64(13), object(5)
memory usage: 5.3+ MB

# drop the Booking_ID column from the dataframe
df= df.drop(columns=["Booking_ID"])

df.head()

df.describe(include="all").T

df.duplicated().sum()

10275

df.isnull().sum().any()

False

df.isnull().sum()

no_of_adults                            0
no_of_children                          0
no_of_weekend_nights                    0
no_of_week_nights                       0
type_of_meal_plan                       0
required_car_parking_space              0
room_type_reserved                      0
lead_time                               0
arrival_year                            0
arrival_month                           0
arrival_date                            0
market_segment_type                     0
repeated_guest                          0
no_of_previous_cancellations            0
no_of_previous_bookings_not_canceled    0
avg_price_per_room                      0
no_of_special_requests                  0
booking_status                          0
dtype: int64

numerical_vars = df.select_dtypes(include=['number'])
# 'number' includes int and float types
print("Numerical Variables:")
print(numerical_vars.columns)

Numerical Variables:
Index(['no_of_adults', 'no_of_children', 'no_of_weekend_nights',
       'no_of_week_nights', 'required_car_parking_space', 'lead_time',
       'arrival_year', 'arrival_month', 'arrival_date', 'repeated_guest',
       'no_of_previous_cancellations', 'no_of_previous_bookings_not_canceled',
       'avg_price_per_room', 'no_of_special_requests'],
      dtype='object')

categorical_vars = df.select_dtypes(include=['object'])
# 'object' data type usually represents categorical variables
print("\nCategorical Variables:")
print(categorical_vars.columns)

Categorical Variables:
Index(['type_of_meal_plan', 'room_type_reserved', 'market_segment_type',
       'booking_status'],
      dtype='object')

# function to create histogram-boxplot visualization

def histogram_boxplot(data, feature, figsize=(15, 10), kde=False, bins=None):
    """
    Boxplot and histogram combined

    data: dataframe
    feature: dataframe column
    figsize: size of figure (default (15,10))
    kde: whether to show the density curve (default False)
    bins: number of bins for histogram (default None)
    """
    f2, (ax_box2, ax_hist2) = plt.subplots(
        nrows=2,  # Number of rows of the subplot grid= 2
        sharex=True,  # x-axis will be shared among all subplots
        gridspec_kw={"height_ratios": (0.25, 0.75)},
        figsize=figsize,
    )  # creating the 2 subplots
    sns.boxplot(
        data=data, x=feature, ax=ax_box2, showmeans=True, color="violet"
    )  # boxplot will be created and a triangle will indicate the mean value of the column
    sns.histplot(
        data=data, x=feature, kde=kde, ax=ax_hist2, bins=bins
    ) if bins else sns.histplot(
        data=data, x=feature, kde=kde, ax=ax_hist2
    )  # For histogram
    ax_hist2.axvline(
        data[feature].mean(), color="green", linestyle="--"
    )  # Add mean to the histogram
    ax_hist2.axvline(
        data[feature].median(), color="black", linestyle="-"
    )  # Add median to the histogram

# function to create labeled barplots


def labeled_barplot(data, feature, perc=False, n=None):
    """
    Barplot with percentage at the top

    data: dataframe
    feature: dataframe column
    perc: whether to display percentages instead of count (default is False)
    n: displays the top n category levels (default is None, i.e., display all levels)
    """

    total = len(data[feature])  # length of the column
    count = data[feature].nunique()
    if n is None:
        plt.figure(figsize=(count + 2, 6))
    else:
        plt.figure(figsize=(n + 2, 6))

    plt.xticks(rotation=90, fontsize=15)
    ax = sns.countplot(
        data=data,
        x=feature,
        order=data[feature].value_counts().index[:n],
    )

    for p in ax.patches:
        if perc == True:
            label = "{:.1f}%".format(
                100 * p.get_height() / total
            )  # percentage of each class of the category
        else:
            label = p.get_height()  # count of each level of the category

        x = p.get_x() + p.get_width() / 2  # width of the plot
        y = p.get_height()  # height of the plot

        ax.annotate(
            label,
            (x, y),
            ha="center",
            va="center",
            size=12,
            xytext=(0, 5),
            textcoords="offset points",
        )  # annotate the percentage

    plt.show()  # show the plot

df.no_of_previous_cancellations.unique()

array([ 0,  3,  1,  2, 11,  4,  5, 13,  6], dtype=int64)

histogram_boxplot(df, "no_of_previous_cancellations")

# create a labeled barplot for Number of previous cancellations
labeled_barplot(df,"no_of_previous_cancellations")

df.no_of_previous_bookings_not_canceled.unique()

array([ 0,  5,  1,  3,  4, 12, 19,  2, 15, 17,  7, 20, 16, 50, 13,  6, 14,
       34, 18,  8, 10, 23, 11, 49, 47, 53,  9, 33, 22, 24, 52, 21, 48, 28,
       39, 25, 31, 38, 26, 51, 42, 37, 35, 56, 44, 27, 32, 55, 45, 30, 57,
       46, 54, 43, 58, 41, 29, 40, 36], dtype=int64)

histogram_boxplot(df, "no_of_previous_bookings_not_canceled")

df.lead_time.unique()

array([224,   5,   1, 211,  48, 346,  34,  83, 121,  44,   0,  35,  30,
        95,  47, 256,  99,  12, 122,   2,  37, 130,  60,  56,   3, 107,
        72,  23, 289, 247, 186,  64,  96,  41,  55, 146,  32,  57,   7,
       124, 169,   6,  51,  13, 100, 139, 117,  39,  86,  19, 192, 179,
        26,  74, 143, 177,  18, 267, 155,  46, 128,  20,  40, 196, 188,
        17, 110,  68,  73,  92, 171, 134, 320, 118, 189,  16,  24,   8,
        10, 182, 116, 123, 105, 443, 317, 286, 148,  14,  85,  25,  28,
        80,  11, 162,  82,  27, 245, 266, 112,  88,  69, 273,   4,  97,
        31,  62, 197, 280, 185, 160, 104,  22, 292, 109, 126, 303,  81,
        54,  15, 161, 147,  87, 127, 418, 156,  58, 433, 111, 195, 119,
        59,  78, 335, 103,  70,  76, 144,  49,  77,  36,  79,  21,  33,
       164, 152,  43, 102,  71, 209,  93,  53, 302, 239,  45, 167, 113,
        84,   9, 166, 174,  61, 151,  52,  67, 282,  38, 175,  89, 133,
        65,  66,  50, 159, 386, 115, 237, 125,  91,  29, 221, 213, 198,
        75, 180, 236, 120, 230,  63, 136, 309, 157, 268, 217,  94, 305,
        98,  42, 154, 330, 137, 184, 232, 304, 114, 257, 265, 191, 101,
       259, 149, 170, 271, 207, 108, 210, 222, 296, 194, 145, 153, 275,
       158, 301, 349, 200, 315, 181, 263, 176, 141, 270, 150, 359, 244,
       219, 142, 138, 276, 178, 163, 377, 290, 216, 226, 258, 254, 193,
       131, 208, 215, 190, 381, 231, 248, 106, 308, 140, 173, 168, 172,
        90, 249, 205, 129, 212, 135, 220, 277, 253, 132, 183, 255, 223,
       336, 288, 229, 319, 199, 203, 228, 246, 235, 294, 281, 202, 361,
       287, 291, 313, 206, 269, 279, 261, 214, 274, 250, 187, 240, 241,
       323, 322, 227, 225, 233, 338, 283, 327, 204, 352, 165, 251, 299,
       314, 285, 238, 328, 278, 332, 243, 201, 307, 272, 252, 242, 284,
       297, 324, 260, 262, 326, 295, 218, 234, 353, 300, 355, 306, 298,
       331, 341, 318, 333, 372, 311, 310, 345, 264, 325, 293, 348, 350,
       351], dtype=int64)

histogram_boxplot(df, "lead_time")

df.no_of_adults.unique()

array([2, 1, 3, 0, 4], dtype=int64)

histogram_boxplot(df, "no_of_adults")

# create a labeled barplot for Number of Adults
labeled_barplot(df,"no_of_adults")

df.no_of_children.unique()

array([ 0,  2,  1,  3, 10,  9], dtype=int64)

histogram_boxplot(df, "no_of_children")

# replacing 9, and 10 children with 3, other above 3 portray outliers
df["no_of_children"] = df["no_of_children"].replace([9, 10], 3)

# create a labeled barplot for Number of Children
labeled_barplot(df,"no_of_children")

df.no_of_week_nights.unique()

array([ 2,  3,  1,  4,  5,  0, 10,  6, 11,  7, 15,  9, 13,  8, 14, 12, 17,
       16], dtype=int64)

histogram_boxplot(df, "no_of_week_nights")

# create a labeled barplot for Number of Week Nights
labeled_barplot(df,"no_of_week_nights")

df.no_of_weekend_nights.unique()

array([1, 2, 0, 4, 3, 6, 5, 7], dtype=int64)

histogram_boxplot(df, "no_of_weekend_nights")

# create a labeled barplot for Number of Weekend Nights
labeled_barplot(df,"no_of_weekend_nights")

df.required_car_parking_space.unique()

array([0, 1], dtype=int64)

histogram_boxplot(df, "required_car_parking_space")

# create a labeled barplot for Required Car parking Space
labeled_barplot(df,"required_car_parking_space")

df.arrival_year.unique()

array([2017, 2018], dtype=int64)

histogram_boxplot(df, "arrival_year")

# create labeled_barplot for arrival month
labeled_barplot(df, 'arrival_year')

df.arrival_month.unique()

histogram_boxplot(df, "arrival_month")

# create labeled_barplot for arrival month
labeled_barplot(df, 'arrival_month')

df.arrival_date.unique()

array([ 2,  6, 28, 20, 11, 13, 15, 26, 18, 30,  5, 10,  4, 25, 22, 21, 19,
       17,  7,  9, 27,  1, 29, 16,  3, 24, 14, 31, 23,  8, 12],
      dtype=int64)

histogram_boxplot(df, "arrival_date")

 # create labeled_barplot for arrival month
 labeled_barplot(df, 'arrival_date')

df.repeated_guest.unique()

array([0, 1], dtype=int64)

histogram_boxplot(df, "repeated_guest")

# create a labeled barplot for repeated guest
labeled_barplot(df, 'repeated_guest')

df.no_of_special_requests.unique()

array([0, 1, 3, 2, 4, 5], dtype=int64)

  histogram_boxplot(df,"no_of_special_requests")

labeled_barplot(df,"no_of_special_requests")

df.avg_price_per_room.unique()

array([ 65.  , 106.68,  60.  , ..., 118.43, 137.25, 167.8 ])

histogram_boxplot(df, "avg_price_per_room")

# creating a subset of the original DataFrame data containing only those rows where the average price per room is equal to 0
df[df["avg_price_per_room"] == 0]

# identify and count the occurrences of different market segments for bookings where the average price per room is set to 0.
df.loc[df["avg_price_per_room"] == 0, "market_segment_type"].value_counts()

Complementary    354
Online           191
Name: market_segment_type, dtype: int64

# Calculating the 25th quantile
Q1 = df["avg_price_per_room"].quantile(0.25)

# Calculating the 75th quantile
Q3 = df["avg_price_per_room"].quantile(0.75)  ## Complete the code to calculate 75th quantile for average price per room

# Calculating IQR
IQR = Q3 - Q1

# Calculating value of upper whisker
Upper_Whisker = Q3 + 1.5 * IQR
Upper_Whisker

179.55

# assigning the outliers the value of upper whisker
df.loc[df["avg_price_per_room"] >= 500, "avg_price_per_room"] = Upper_Whisker

df.type_of_meal_plan.unique()

array(['Meal Plan 1', 'Not Selected', 'Meal Plan 2', 'Meal Plan 3'],
      dtype=object)

# Plot the labeled barplot for the type of meal plan
labeled_barplot(df, "type_of_meal_plan", perc=True, n=10)

df.room_type_reserved.unique()

array(['Room_Type 1', 'Room_Type 4', 'Room_Type 2', 'Room_Type 6',
       'Room_Type 5', 'Room_Type 7', 'Room_Type 3'], dtype=object)

# Plot the labeled barplot for the room type reserved
labeled_barplot(df, "room_type_reserved", perc=True, n=10)

df.market_segment_type.unique()

array(['Offline', 'Online', 'Corporate', 'Aviation', 'Complementary'],
      dtype=object)

# Plot the labeled barplot for the market segment type
labeled_barplot(df, "market_segment_type", perc=True, n=10)

df.booking_status.unique()

array(['Not_Canceled', 'Canceled'], dtype=object)

# Plot the labeled barplot for Booking Status
labeled_barplot(df, "booking_status", perc=True, n=10)

df["booking_status"] = df["booking_status"].apply(
    lambda x: 1 if x == "Canceled" else 0
)

cols_list = df.select_dtypes(include=np.number).columns.tolist()

plt.figure(figsize=(12, 7))
sns.heatmap(df[cols_list].corr(), annot=True, vmin=-1, vmax=1, fmt=".2f", cmap="Spectral")
plt.show()

### function to plot distributions wrt target


def distribution_plot_wrt_target(data, predictor, target):

    fig, axs = plt.subplots(2, 2, figsize=(12, 10))

    target_uniq = data[target].unique()

    axs[0, 0].set_title("Distribution of target for target=" + str(target_uniq[0]))
    sns.histplot(
        data=data[data[target] == target_uniq[0]],
        x=predictor,
        kde=True,
        ax=axs[0, 0],
        color="teal",
        stat="density",
    )

    axs[0, 1].set_title("Distribution of target for target=" + str(target_uniq[1]))
    sns.histplot(
        data=data[data[target] == target_uniq[1]],
        x=predictor,
        kde=True,
        ax=axs[0, 1],
        color="orange",
        stat="density",
    )

    axs[1, 0].set_title("Boxplot w.r.t target")
    sns.boxplot(data=data, x=target, y=predictor, ax=axs[1, 0])

    axs[1, 1].set_title("Boxplot (without outliers) w.r.t target")
    sns.boxplot(
        data=data,
        x=target,
        y=predictor,
        ax=axs[1, 1],
        showfliers=False,
    )

    plt.tight_layout()
    plt.show()

def stacked_barplot(data, predictor, target):
    """
    Print the category counts and plot a stacked bar chart

    data: dataframe
    predictor: independent variable
    target: target variable
    """
    count = data[predictor].nunique()
    sorter = data[target].value_counts().index[-1]
    tab1 = pd.crosstab(data[predictor], data[target], margins=True).sort_values(
        by=sorter, ascending=False
    )
    print(tab1)
    print("-" * 120)
    tab = pd.crosstab(data[predictor], data[target], normalize="index").sort_values(
        by=sorter, ascending=False
    )
    tab.plot(kind="bar", stacked=True, figsize=(count + 5, 5))
    plt.legend(
        loc="lower left", frameon=False,
    )
    plt.legend(loc="upper left", bbox_to_anchor=(1, 1))
    plt.show()

 # Calculate and display summary statistics of room prices by market segment
segment_summary = df.groupby('market_segment_type')['avg_price_per_room'].describe()

# Create a box plot to show the variation in room prices across different market segments
plt.figure(figsize=(12, 8))
sns.boxplot(x='market_segment_type', y='avg_price_per_room', data=df, palette='Set2')
plt.title('Variation in Room Prices Across Market Segments')
plt.xlabel('Market Segment')
plt.ylabel('Price per Room')
plt.xticks(rotation=45)  # Rotate x-labels for better readability
plt.grid(axis='y', linestyle='--', alpha=0.7)

# Display a summary of room price variation by market segment
print("Summary of Room Price Variation Across Market Segments:")
print(segment_summary)

plt.show()

Summary of Room Price Variation Across Market Segments:
                          count      mean      std      min      25%  \
market_segment_type                                                    
Aviation              125.00000 100.70400  8.53836 79.00000 95.00000   
Complementary         391.00000   3.14176 15.51297  0.00000  0.00000   
Corporate            2017.00000  82.91174 23.69000 31.00000 65.00000   
Offline             10528.00000  91.59844 24.62552 12.00000 75.00000   
Online              23214.00000 112.25685 35.22032  0.00000 89.00000   

                          50%       75%       max  
market_segment_type                                
Aviation             95.00000 110.00000 110.00000  
Complementary         0.00000   0.00000 170.00000  
Corporate            79.00000  95.00000 220.00000  
Offline              90.00000 109.00000 365.00000  
Online              107.10000 131.75000 375.50000

stacked_barplot(df, "market_segment_type", "repeated_guest")

repeated_guest           0    1    All
market_segment_type                   
All                  35345  930  36275
Corporate             1415  602   2017
Complementary          265  126    391
Online               23118   96  23214
Offline              10438   90  10528
Aviation               109   16    125
------------------------------------------------------------------------------------------------------------------------

stacked_barplot(df,'no_of_special_requests' ,'no_of_previous_cancellations')

no_of_previous_cancellations      0    1   2   3   4   5  6  11  13    All
no_of_special_requests                                                    
2                              4347    4   1   2   1   7  1   1   0   4364
All                           35937  198  46  43  10  11  1  25   4  36275
0                             19553  153  25  28   3   3  0   8   4  19777
1                             11284   39  14  13   6   1  0  16   0  11373
3                               668    1   6   0   0   0  0   0   0    675
4                                77    1   0   0   0   0  0   0   0     78
5                                 8    0   0   0   0   0  0   0   0      8
------------------------------------------------------------------------------------------------------------------------

#create boxplot for no of special requests and average price per room (excluding the outliers)
plt.figure(figsize=(10, 5))
# Create a boxplot with outliers excluded using the 'whis' parameter set to 1.5
sns.boxplot(df, x='no_of_special_requests', y='avg_price_per_room', showfliers=False)  
plt.show()

stacked_barplot(df,'booking_status' ,'no_of_previous_cancellations')

no_of_previous_cancellations      0    1   2   3   4   5  6  11  13    All
booking_status                                                            
0                             24068  187  46  42  10  11  1  25   0  24390
All                           35937  198  46  43  10  11  1  25   4  36275
1                             11869   11   0   1   0   0  0   0   4  11885
------------------------------------------------------------------------------------------------------------------------

#Filter for repeating guests
repeating_guests = df[df['repeated_guest'] == 1]

# Calculate the percentage of repeating guests with a certain number of previous cancellations who cancel
cancellation_percentage_per_repeated_guest = repeating_guests.groupby('no_of_previous_cancellations')['booking_status'].mean() * 100
cancellation_percentage_per_repeated_guest = cancellation_percentage_per_repeated_guest.reset_index()

# Plotting the percentage of repeating guests with a certain number of previous cancellations who cancel using a bar plot
plt.figure(figsize=(10, 6))
ax = sns.barplot(x='no_of_previous_cancellations', y='booking_status', data=cancellation_percentage_per_repeated_guest, color='skyblue')

# Annotate each bar with the percentage value
for p in ax.patches:
    height = p.get_height()
    ax.text(p.get_x() + p.get_width() / 2, height, '{:.2f}%'.format(height), ha='center', va='bottom')

plt.title('Cancellation Percentage of Repeating Guests with Previous Cancellations')
plt.xlabel('Number of Previous Cancellations')
plt.ylabel('Cancellation Percentage')
plt.grid(axis='y', linestyle='--', alpha=0.7)
plt.show()

print("Number of Previous Cancellations per Repeated Guests:")
print(cancellation_percentage_per_repeated_guest)

Number of Previous Cancellations per Repeated Guests:
   no_of_previous_cancellations  booking_status
0                             0         0.00000
1                             1         5.55556
2                             2         0.00000
3                             3         2.32558
4                             4         0.00000
5                             5         0.00000
6                             6         0.00000
7                            11         0.00000
8                            13       100.00000

stacked_barplot(df, 'repeated_guest', 'booking_status')

booking_status      0      1    All
repeated_guest                     
All             24390  11885  36275
0               23476  11869  35345
1                 914     16    930
------------------------------------------------------------------------------------------------------------------------

family_data = df[(df["no_of_children"] >= 0) & (df["no_of_adults"] > 1)]
family_data.shape

(28441, 18)

family_data["no_of_family_members"] = (family_data["no_of_adults"] + family_data["no_of_children"])

# stacked barplot for no of family members and booking status
stacked_barplot(family_data, 'no_of_family_members', 'booking_status' )

booking_status            0     1    All
no_of_family_members                    
All                   18456  9985  28441
2                     15506  8213  23719
3                      2425  1368   3793
4                       514   398    912
5                        11     6     17
------------------------------------------------------------------------------------------------------------------------

# Calculate the number of bookings and their corresponding counts of adults and children
booking_counts = df.groupby(['no_of_adults', 'no_of_children']).size().reset_index(name='Booking_Count')

# Sorting the booking counts by total number of guests
booking_counts['Total_Guests'] = booking_counts['no_of_adults'] + booking_counts['no_of_children']
booking_counts = booking_counts.sort_values('Total_Guests').reset_index(drop=True)

# Plotting the data using a bar plot with annotations for no. of adults and children breakdown
plt.figure(figsize=(12, 8))
sns.barplot(x=[f'Booking {i}: {row["no_of_adults"]} Adults, {row["no_of_children"]} Children' for i, row in booking_counts.iterrows()],
            y='Booking_Count', data=booking_counts, palette='Set2')
plt.xlabel('Booking Index')
plt.ylabel('Number of Bookings')
plt.title('Number of Bookings and Counts of Adults/Children')
plt.xticks(rotation=45, ha="right")
plt.grid(axis='y', linestyle='--', alpha=0.7)

# Add annotations with the count of bookings on each bar
for index, row in booking_counts.iterrows():
    plt.text(index, row['Booking_Count'], row['Booking_Count'], ha='center', va='bottom')

plt.tight_layout()
plt.show()

stacked_barplot(df, "market_segment_type", "booking_status")

booking_status           0      1    All
market_segment_type                     
All                  24390  11885  36275
Online               14739   8475  23214
Offline               7375   3153  10528
Corporate             1797    220   2017
Aviation                88     37    125
Complementary          391      0    391
------------------------------------------------------------------------------------------------------------------------

#plot stacked barplot for no of special requests and booking status
stacked_barplot(df,'no_of_special_requests' ,'no_of_previous_cancellations')

no_of_previous_cancellations      0    1   2   3   4   5  6  11  13    All
no_of_special_requests                                                    
2                              4347    4   1   2   1   7  1   1   0   4364
All                           35937  198  46  43  10  11  1  25   4  36275
0                             19553  153  25  28   3   3  0   8   4  19777
1                             11284   39  14  13   6   1  0  16   0  11373
3                               668    1   6   0   0   0  0   0   0    675
4                                77    1   0   0   0   0  0   0   0     78
5                                 8    0   0   0   0   0  0   0   0      8
------------------------------------------------------------------------------------------------------------------------

plt.figure(figsize=(10, 5))
# Create a boxplot with outliers excluded using the 'whis' parameter set to 1.5
sns.boxplot(df, x='no_of_special_requests', y='avg_price_per_room', showfliers=False) ## Complete the code to create boxplot for no of special requests and average price per room (excluding the outliers)
plt.show()

distribution_plot_wrt_target(df, "avg_price_per_room", "booking_status")

distribution_plot_wrt_target(df,'booking_status', 'lead_time') ## Complete the code to find distribution of lead time wrt booking status

One_day_stay_data = df[(df["no_of_week_nights"] > 0) & (df["no_of_weekend_nights"] > 0)]
One_day_stay_data.shape

(17094, 18)

One_day_stay_data["total_days"] = (
    One_day_stay_data["no_of_week_nights"] + One_day_stay_data["no_of_weekend_nights"]
)

stacked_barplot(One_day_stay_data, "total_days", 'booking_status')

booking_status      0     1    All
total_days                        
All             10979  6115  17094
3                3689  2183   5872
4                2977  1387   4364
5                1593   738   2331
2                1301   639   1940
6                 566   465   1031
7                 590   383    973
8                 100    79    179
10                 51    58    109
9                  58    53    111
14                  5    27     32
15                  5    26     31
13                  3    15     18
12                  9    15     24
11                 24    15     39
20                  3     8     11
19                  1     5      6
16                  1     5      6
17                  1     4      5
18                  0     3      3
21                  1     3      4
22                  0     2      2
23                  1     1      2
24                  0     1      1
------------------------------------------------------------------------------------------------------------------------

stacked_barplot(df, 'arrival_month', 'booking_status')

booking_status      0      1    All
arrival_month                      
All             24390  11885  36275
10               3437   1880   5317
9                3073   1538   4611
8                2325   1488   3813
7                1606   1314   2920
6                1912   1291   3203
4                1741    995   2736
5                1650    948   2598
11               2105    875   2980
3                1658    700   2358
2                1274    430   1704
12               2619    402   3021
1                 990     24   1014
------------------------------------------------------------------------------------------------------------------------

# create lineplot between average price per room and arrival month
plt.figure(figsize=(10, 5))
sns.lineplot(df, x='avg_price_per_room', y='arrival_month') 
plt.show()

# Check the number of duplicate rows
duplicate_rows = df[df.duplicated()]
num_duplicates = len(duplicate_rows)

# Print the number of duplicate rows
print(f"Number of duplicate rows: {num_duplicates}")

Number of duplicate rows: 10275

# Examine the distribution of the duplicates
duplicate_rows.describe()

# Investigate the impact of removing the duplicates
df_without_duplicates = df.drop_duplicates()

df_without_duplicates.describe()

numeric_columns = df_without_duplicates.select_dtypes(include=['float64', 'int64']).columns

# Compare the means of each numeric column
for column in numeric_columns:
    if column not in duplicate_rows.columns:
        continue  # Skip if column not present in duplicate_rows
    mean_without_duplicates = df_without_duplicates[column].mean()
    mean_with_duplicates = duplicate_rows[column].mean()
    print(f"Mean of {column} without duplicates: {mean_without_duplicates}")
    print(f"Mean of {column} with duplicates: {mean_with_duplicates}")

Mean of no_of_adults without duplicates: 1.89
Mean of no_of_adults with duplicates: 1.7309975669099757
Mean of no_of_children without duplicates: 0.1405
Mean of no_of_children with duplicates: 0.014306569343065694
Mean of no_of_weekend_nights without duplicates: 0.8823846153846154
Mean of no_of_weekend_nights with duplicates: 0.6293917274939173
Mean of no_of_week_nights without duplicates: 2.2619230769230767
Mean of no_of_week_nights with duplicates: 2.058491484184915
Mean of required_car_parking_space without duplicates: 0.04211538461538462
Mean of required_car_parking_space with duplicates: 0.0028223844282238442
Mean of lead_time without duplicates: 66.45284615384615
Mean of lead_time with duplicates: 132.75299270072992
Mean of arrival_year without duplicates: 2017.8502692307693
Mean of arrival_year with duplicates: 2017.744914841849
Mean of arrival_month without duplicates: 7.332923076923077
Mean of arrival_month with duplicates: 7.65323600973236
Mean of arrival_date without duplicates: 15.75023076923077
Mean of arrival_date with duplicates: 15.209245742092458
Mean of repeated_guest without duplicates: 0.032769230769230766
Mean of repeated_guest with duplicates: 0.0075912408759124085
Mean of no_of_previous_cancellations without duplicates: 0.028538461538461537
Mean of no_of_previous_cancellations with duplicates: 0.010218978102189781
Mean of no_of_previous_bookings_not_canceled without duplicates: 0.21311538461538462
Mean of no_of_previous_bookings_not_canceled with duplicates: 0.0023357664233576644
Mean of avg_price_per_room without duplicates: 105.68197500000001
Mean of avg_price_per_room with duplicates: 97.67368175182483
Mean of no_of_special_requests without duplicates: 0.7419615384615385
Mean of no_of_special_requests with duplicates: 0.31017031630170316
Mean of booking_status without duplicates: 0.28623076923076923
Mean of booking_status with duplicates: 0.4324087591240876

numeric_columns = df.select_dtypes(include=np.number).columns.tolist()

#dropping booking status
numeric_columns.remove("booking_status")
plt.figure(figsize=(15, 12))

for i, variable in enumerate(numeric_columns):
    plt.subplot(4, 4, i + 1)
    plt.boxplot(df[variable], whis=1.5)
    plt.tight_layout()
    plt.title(variable)

cols_list = df.select_dtypes(include=np.number).columns.tolist()

plt.figure(figsize=(12, 7))
sns.heatmap(df[cols_list].corr(), annot=True, vmin=-1, vmax=1, fmt=".2f", cmap="Spectral")
plt.show()

# defining a function to compute different metrics to check performance of a classification model built using statsmodels
def model_performance_classification_statsmodels(
    model, predictors, target, threshold=0.5
):
    """
    Function to compute different metrics to check classification model performance

    model: classifier
    predictors: independent variables
    target: dependent variable
    threshold: threshold for classifying the observation as class 1
    """

    # checking which probabilities are greater than threshold
    pred_temp = model.predict(predictors) > threshold
    # rounding off the above values to get classes
    pred = np.round(pred_temp)

    acc = accuracy_score(target, pred)  # to compute Accuracy
    recall = recall_score(target, pred)  # to compute Recall
    precision = precision_score(target, pred)  # to compute Precision
    f1 = f1_score(target, pred)  # to compute F1-score

    # creating a dataframe of metrics
    df_perf = pd.DataFrame(
        {"Accuracy": acc, "Recall": recall, "Precision": precision, "F1": f1,},
        index=[0],
    )

    return df_perf

# defining a function to plot the confusion_matrix of a classification model


def confusion_matrix_statsmodels(model, predictors, target, threshold=0.5):
    """
    To plot the confusion_matrix with percentages

    model: classifier
    predictors: independent variables
    target: dependent variable
    threshold: threshold for classifying the observation as class 1
    """
    y_pred = model.predict(predictors) > threshold
    cm = confusion_matrix(target, y_pred)
    labels = np.asarray(
        [
            ["{0:0.0f}".format(item) + "\n{0:.2%}".format(item / cm.flatten().sum())]
            for item in cm.flatten()
        ]
    ).reshape(2, 2)

    plt.figure(figsize=(6, 4))
    sns.heatmap(cm, annot=labels, fmt="")
    plt.ylabel("True label")
    plt.xlabel("Predicted label")

# separating independent variables from dependent variables
X = df.drop(["booking_status"], axis=1) ## Independent variables
Y = df["booking_status"] ## dependent variable

# adding constant to X
X = add_constant(X)
X =  sm.add_constant(X) 

# create dummies for X
X = X = pd.get_dummies(X, drop_first=True) 


# Splitting data in train and test sets
X_train, X_test, y_train, y_test = train_test_split( X, Y, test_size=0.30, random_state=1, stratify=Y
) ## split the data into train test in the ratio 70:30 with random_state = 1

print("Shape of Training set : ", X_train.shape)
print("Shape of test set : ", X_test.shape)
print("Percentage of classes in training set:")
print(y_train.value_counts(normalize=True))
print("Percentage of classes in test set:")
print(y_test.value_counts(normalize=True))

Shape of Training set :  (25392, 28)
Shape of test set :  (10883, 28)
Percentage of classes in training set:
0   0.67238
1   0.32762
Name: booking_status, dtype: float64
Percentage of classes in test set:
0   0.67233
1   0.32767
Name: booking_status, dtype: float64

# fitting logistic regression model
logit = sm.Logit(y_train, X_train.astype(float))
lg = logit.fit() ## fit logistic regression
print(lg.summary()) ## print summary of the model

Warning: Maximum number of iterations has been exceeded.
         Current function value: 0.422404
         Iterations: 35
                           Logit Regression Results                           
==============================================================================
Dep. Variable:         booking_status   No. Observations:                25392
Model:                          Logit   Df Residuals:                    25364
Method:                           MLE   Df Model:                           27
Date:                Fri, 19 Apr 2024   Pseudo R-squ.:                  0.3321
Time:                        02:55:56   Log-Likelihood:                -10726.
converged:                      False   LL-Null:                       -16060.
Covariance Type:            nonrobust   LLR p-value:                     0.000
========================================================================================================
                                           coef    std err          z      P>|z|      [0.025      0.975]
--------------------------------------------------------------------------------------------------------
const                                 -886.9564    121.335     -7.310      0.000   -1124.769    -649.144
no_of_adults                             0.0323      0.038      0.856      0.392      -0.042       0.106
no_of_children                           0.0680      0.063      1.075      0.282      -0.056       0.192
no_of_weekend_nights                     0.1461      0.020      7.367      0.000       0.107       0.185
no_of_week_nights                        0.0354      0.012      2.881      0.004       0.011       0.059
required_car_parking_space              -1.6149      0.137    -11.772      0.000      -1.884      -1.346
lead_time                                0.0158      0.000     58.942      0.000       0.015       0.016
arrival_year                             0.4382      0.060      7.288      0.000       0.320       0.556
arrival_month                           -0.0476      0.006     -7.336      0.000      -0.060      -0.035
arrival_date                             0.0030      0.002      1.543      0.123      -0.001       0.007
repeated_guest                          -1.9184      0.767     -2.502      0.012      -3.421      -0.416
no_of_previous_cancellations             0.3476      0.102      3.413      0.001       0.148       0.547
no_of_previous_bookings_not_canceled    -1.3824      0.906     -1.527      0.127      -3.157       0.393
avg_price_per_room                       0.0185      0.001     24.946      0.000       0.017       0.020
no_of_special_requests                  -1.4899      0.030    -48.954      0.000      -1.550      -1.430
type_of_meal_plan_Meal Plan 2            0.1732      0.067      2.586      0.010       0.042       0.305
type_of_meal_plan_Meal Plan 3           14.0607    764.119      0.018      0.985   -1483.585    1511.707
type_of_meal_plan_Not Selected           0.1986      0.053      3.724      0.000       0.094       0.303
room_type_reserved_Room_Type 2          -0.4087      0.134     -3.061      0.002      -0.670      -0.147
room_type_reserved_Room_Type 3           1.1880      1.891      0.628      0.530      -2.519       4.895
room_type_reserved_Room_Type 4          -0.2697      0.053     -5.050      0.000      -0.374      -0.165
room_type_reserved_Room_Type 5          -0.6814      0.215     -3.170      0.002      -1.103      -0.260
room_type_reserved_Room_Type 6          -0.8243      0.155     -5.317      0.000      -1.128      -0.520
room_type_reserved_Room_Type 7          -1.3507      0.298     -4.536      0.000      -1.934      -0.767
market_segment_type_Complementary      -22.8028   1590.884     -0.014      0.989   -3140.878    3095.273
market_segment_type_Corporate           -0.8518      0.276     -3.088      0.002      -1.392      -0.311
market_segment_type_Offline             -1.7631      0.264     -6.683      0.000      -2.280      -1.246
market_segment_type_Online               0.0082      0.261      0.031      0.975      -0.503       0.520
========================================================================================================

# Performance check on X_train1 and y_train
print("Training performance:")
model_performance_classification_statsmodels(lg, X_train, y_train)

Training performance:

# define a function to check VIF
def checking_vif(predictors):
    vif = pd.DataFrame()
    vif["feature"] = predictors.columns

    # calculating VIF for each feature
    vif["VIF"] = [
        variance_inflation_factor(predictors.values, i)
        for i in range(len(predictors.columns))
    ]
    return vif

checking_vif(X_train)

# initial list of columns
cols = X_train.columns.tolist()

# setting an initial max p-value
max_p_value = 1

while len(cols) > 0:
    # defining the train set
    x_train_aux = X_train[cols]

    # fitting the model
    model = sm.Logit(y_train, x_train_aux).fit(disp=False)

    # getting the p-values and the maximum p-value
    p_values = model.pvalues
    max_p_value = max(p_values)

    # name of the variable with maximum p-value
    feature_with_p_max = p_values.idxmax()

    if max_p_value > 0.05:
        cols.remove(feature_with_p_max)
    else:
        break

selected_features = cols
print(selected_features)

['const', 'no_of_weekend_nights', 'no_of_week_nights', 'required_car_parking_space', 'lead_time', 'arrival_year', 'arrival_month', 'repeated_guest', 'no_of_previous_cancellations', 'avg_price_per_room', 'no_of_special_requests', 'type_of_meal_plan_Meal Plan 2', 'type_of_meal_plan_Not Selected', 'room_type_reserved_Room_Type 2', 'room_type_reserved_Room_Type 4', 'room_type_reserved_Room_Type 5', 'room_type_reserved_Room_Type 6', 'room_type_reserved_Room_Type 7', 'market_segment_type_Corporate', 'market_segment_type_Offline']

# Specify the train and test data with selected features
X_train1 = X_train[selected_features]
X_test1 = X_test[selected_features]

# create a function for logistic Regression Model 
logit1 = sm.Logit(y_train, X_train1) ## function to train logistic regression on X_train1 and y_train
lg1 = logit1.fit() ## function to fit logistic regression
print(lg1.summary()) ## print summary of the model

Optimization terminated successfully.
         Current function value: 0.423028
         Iterations 11
                           Logit Regression Results                           
==============================================================================
Dep. Variable:         booking_status   No. Observations:                25392
Model:                          Logit   Df Residuals:                    25372
Method:                           MLE   Df Model:                           19
Date:                Fri, 19 Apr 2024   Pseudo R-squ.:                  0.3312
Time:                        02:56:50   Log-Likelihood:                -10742.
converged:                       True   LL-Null:                       -16060.
Covariance Type:            nonrobust   LLR p-value:                     0.000
==================================================================================================
                                     coef    std err          z      P>|z|      [0.025      0.975]
--------------------------------------------------------------------------------------------------
const                           -869.4077    120.911     -7.191      0.000   -1106.388    -632.427
no_of_weekend_nights               0.1497      0.020      7.564      0.000       0.111       0.189
no_of_week_nights                  0.0362      0.012      2.950      0.003       0.012       0.060
required_car_parking_space        -1.6151      0.137    -11.783      0.000      -1.884      -1.346
lead_time                          0.0159      0.000     59.906      0.000       0.015       0.016
arrival_year                       0.4295      0.060      7.168      0.000       0.312       0.547
arrival_month                     -0.0493      0.006     -7.614      0.000      -0.062      -0.037
repeated_guest                    -3.0741      0.597     -5.151      0.000      -4.244      -1.904
no_of_previous_cancellations       0.2891      0.078      3.721      0.000       0.137       0.441
avg_price_per_room                 0.0191      0.001     26.685      0.000       0.018       0.020
no_of_special_requests            -1.4855      0.030    -49.274      0.000      -1.545      -1.426
type_of_meal_plan_Meal Plan 2      0.1657      0.067      2.475      0.013       0.035       0.297
type_of_meal_plan_Not Selected     0.2100      0.053      3.977      0.000       0.106       0.313
room_type_reserved_Room_Type 2    -0.3733      0.129     -2.890      0.004      -0.627      -0.120
room_type_reserved_Room_Type 4    -0.2685      0.052     -5.203      0.000      -0.370      -0.167
room_type_reserved_Room_Type 5    -0.6889      0.214     -3.216      0.001      -1.109      -0.269
room_type_reserved_Room_Type 6    -0.7440      0.120     -6.203      0.000      -0.979      -0.509
room_type_reserved_Room_Type 7    -1.3177      0.292     -4.512      0.000      -1.890      -0.745
market_segment_type_Corporate     -0.8710      0.103     -8.444      0.000      -1.073      -0.669
market_segment_type_Offline       -1.7713      0.052    -34.275      0.000      -1.873      -1.670
==================================================================================================

# Performance check on X_train1 and y_train
print("Training performance:")
model_performance_classification_statsmodels(lg1, X_train1, y_train)

Training performance:

# converting coefficients to odds
odds = np.exp(lg1.params)

# finding the percentage change
perc_change_odds = (np.exp(lg1.params) - 1) * 100

# removing limit from number of columns to display
pd.set_option("display.max_columns", None)

# adding the odds to a dataframe
pd.DataFrame({"Odds": odds, "Change_odd%": perc_change_odds}, index=X_train1.columns).T

# creating confusion matrix
confusion_matrix_statsmodels(lg1, X_train1, y_train)

print("Training performance:")
log_reg_model_train_perf = model_performance_classification_statsmodels(lg1, X_train1, y_train) ## Complete the code to check performance on X_train1 and y_train
log_reg_model_train_perf

Training performance:

logit_roc_auc_train = roc_auc_score(y_train, lg1.predict(X_train1))
fpr, tpr, thresholds = roc_curve(y_train, lg1.predict(X_train1))
plt.figure(figsize=(7, 5))
plt.plot(fpr, tpr, label="Logistic Regression (area = %0.2f)" % logit_roc_auc_train)
plt.plot([0, 1], [0, 1], "r--")
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.01])
plt.xlabel("False Positive Rate")
plt.ylabel("True Positive Rate")
plt.title("Receiver operating characteristic")
plt.legend(loc="lower right")
plt.show()

# Optimal threshold as per AUC-ROC curve
# The optimal cut off would be where tpr is high and fpr is low
fpr, tpr, thresholds = roc_curve(y_train, lg1.predict(X_train1))

optimal_idx = np.argmax(tpr - fpr)
optimal_threshold_auc_roc = thresholds[optimal_idx]
print(optimal_threshold_auc_roc)

0.33300753336834654

# creating confusion matrix
confusion_matrix_statsmodels(
    lg1, X_train1, y_train,
) ##  create the confusion matrix for X_train1 and y_train with optimal_threshold_auc_roc as threshold

# checking model performance for this model
log_reg_model_train_perf_threshold_auc_roc = model_performance_classification_statsmodels(
    lg1, X_train1, y_train, threshold=optimal_threshold_auc_roc
)
print("Training performance:")
log_reg_model_train_perf_threshold_auc_roc

Training performance:

y_scores = lg1.predict(X_train1)
prec, rec, tre = precision_recall_curve(y_train, y_scores,)


def plot_prec_recall_vs_tresh(precisions, recalls, thresholds):
    plt.plot(thresholds, precisions[:-1], "b--", label="precision")
    plt.plot(thresholds, recalls[:-1], "g--", label="recall")
    plt.xlabel("Threshold")
    plt.legend(loc="upper left")
    plt.ylim([0, 1])


plt.figure(figsize=(10, 7))
plot_prec_recall_vs_tresh(prec, rec, tre)
plt.show()

# setting the threshold
optimal_threshold_curve = 0.42

# creating confusion matrix
confusion_matrix_statsmodels(
    lg1, X_train1, y_train, optimal_threshold_curve) ## create the confusion matrix for X_train1 and y_train with optimal_threshold_curve as threshold

log_reg_model_train_perf_threshold_curve = model_performance_classification_statsmodels(
    lg1, X_train1, y_train, threshold=optimal_threshold_curve
)
print("Training performance:")
log_reg_model_train_perf_threshold_curve

Training performance:

# creating confusion matrix
confusion_matrix_statsmodels(lg1, X_test1, y_test) ##  create confusion matrix for X_test1 and y_test

log_reg_model_test_perf = model_performance_classification_statsmodels(lg1, X_test1, y_test) ## Complete the code to check performance on X_test1 and y_test

print("Test performance:")
log_reg_model_test_perf

Test performance:

ogit_roc_auc_train = roc_auc_score(y_test, lg1.predict(X_test1))
fpr, tpr, thresholds = roc_curve(y_test, lg1.predict(X_test1))
plt.figure(figsize=(7, 5))
plt.plot(fpr, tpr, label="Logistic Regression (area = %0.2f)" % logit_roc_auc_train)
plt.plot([0, 1], [0, 1], "r--")
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.01])
plt.xlabel("False Positive Rate")
plt.ylabel("True Positive Rate")
plt.title("Receiver operating characteristic")
plt.legend(loc="lower right")
plt.show()

# creating confusion matrix
confusion_matrix_statsmodels(lg1, X_test1, y_test) ##  create confusion matrix for X_test1 and y_test using optimal_threshold_auc_roc as threshold

# checking model performance for this model
log_reg_model_test_perf_threshold_auc_roc = model_performance_classification_statsmodels(
    lg1, X_test1, y_test, threshold=optimal_threshold_auc_roc
)
print("Test performance:")
log_reg_model_test_perf_threshold_auc_roc

Test performance:

# creating confusion matrix
confusion_matrix_statsmodels(lg1, X_test1, y_test) ## create confusion matrix for X_test1 and y_test using optimal_threshold_curve as threshold

log_reg_model_test_perf_threshold_curve = model_performance_classification_statsmodels(
    lg1, X_test1, y_test, threshold=optimal_threshold_curve
)
print("Test performance:")
log_reg_model_test_perf_threshold_curve

Test performance:

# training performance comparison

models_train_comp_df = pd.concat(
    [
        log_reg_model_train_perf.T,
        log_reg_model_train_perf_threshold_auc_roc.T,
        log_reg_model_train_perf_threshold_curve.T,
    ],
    axis=1,
)
models_train_comp_df.columns = [
    "Logistic Regression-default Threshold",
    "Logistic Regression-0.37 Threshold",
    "Logistic Regression-0.42 Threshold",
]

print("Training performance comparison:")
models_train_comp_df

Training performance comparison:

# test performance comparison
models_test_comp_df = pd.concat(
    [
        log_reg_model_test_perf.T,
        log_reg_model_test_perf_threshold_auc_roc.T,
        log_reg_model_test_perf_threshold_curve.T,
    ],
    axis=1,
)
models_test_comp_df.columns = [
    "Logistic Regression-default Threshold",
    "Logistic Regression-0.37 Threshold",
    "Logistic Regression-0.42 Threshold",
]

print("Test performance comparison:")
models_test_comp_df

Test performance comparison:

X = df.drop(["booking_status"], axis=1)
Y = df["booking_status"]

X = pd.get_dummies(X) ## create dummies for X

# Splitting data in train and test sets
X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.3, random_state=1) ##  split the data into train test in the ratio 70:30 with random_state = 1

print("Shape of Training set : ", X_train.shape)
print("Shape of test set : ", X_test.shape)
print("Percentage of classes in training set:")
print(y_train.value_counts(normalize=True))
print("Percentage of classes in test set:")
print(y_test.value_counts(normalize=True))

Shape of Training set :  (25392, 30)
Shape of test set :  (10883, 30)
Percentage of classes in training set:
0   0.67064
1   0.32936
Name: booking_status, dtype: float64
Percentage of classes in test set:
0   0.67638
1   0.32362
Name: booking_status, dtype: float64

# defining a function to compute different metrics to check performance of a classification model built using sklearn
def model_performance_classification_sklearn(model, predictors, target):
    """
    Function to compute different metrics to check classification model performance

    model: classifier
    predictors: independent variables
    target: dependent variable
    """

    # predicting using the independent variables
    pred = model.predict(predictors)

    acc = accuracy_score(target, pred)  # to compute Accuracy
    recall = recall_score(target, pred)  # to compute Recall
    precision = precision_score(target, pred)  # to compute Precision
    f1 = f1_score(target, pred)  # to compute F1-score

    # creating a dataframe of metrics
    df_perf = pd.DataFrame(
        {"Accuracy": acc, "Recall": recall, "Precision": precision, "F1": f1,},
        index=[0],
    )

    return df_perf

def confusion_matrix_sklearn(model, predictors, target):
    """
    To plot the confusion_matrix with percentages

    model: classifier
    predictors: independent variables
    target: dependent variable
    """
    y_pred = model.predict(predictors)
    cm = confusion_matrix(target, y_pred)
    labels = np.asarray(
        [
            ["{0:0.0f}".format(item) + "\n{0:.2%}".format(item / cm.flatten().sum())]
            for item in cm.flatten()
        ]
    ).reshape(2, 2)

    plt.figure(figsize=(6, 4))
    sns.heatmap(cm, annot=labels, fmt="")
    plt.ylabel("True label")
    plt.xlabel("Predicted label")

# Fit the DecisionTreeClassifier model on the training data
model = DecisionTreeClassifier(random_state=1)
model.fit(X_train, y_train)

DecisionTreeClassifier(random_state=1)

DecisionTreeClassifier(random_state=1)

#Complete the code to create confusion matrix for train data
confusion_matrix_sklearn(model, X_train, y_train) ## create confusion matrix for train data

decision_tree_perf_train = model_performance_classification_sklearn(
    model, X_train, y_train
)
decision_tree_perf_train

confusion_matrix_sklearn(model, X_test, y_test) ## create confusion matrix for test data

decision_tree_perf_test = model_performance_classification_sklearn(model, X_test, y_test) ## check performance on test set
decision_tree_perf_test

feature_names = list(X_train.columns)
importances = model.feature_importances_
indices = np.argsort(importances)

plt.figure(figsize=(8, 8))
plt.title("Feature Importances")
plt.barh(range(len(indices)), importances[indices], color="violet", align="center")
plt.yticks(range(len(indices)), [feature_names[i] for i in indices])
plt.xlabel("Relative Importance")
plt.show()

# Choose the type of classifier.
estimator = DecisionTreeClassifier(random_state=1, class_weight="balanced")

# Grid of parameters to choose from
parameters = {
    "max_depth": np.arange(2, 7, 2),
    "max_leaf_nodes": [50, 75, 150, 250],
    "min_samples_split": [10, 30, 50, 70],
}

# Type of scoring used to compare parameter combinations
acc_scorer = make_scorer(f1_score)

# Run the grid search
grid_obj = GridSearchCV(estimator, parameters, scoring=acc_scorer, cv=5)
grid_obj = grid_obj.fit(X_train, y_train)

# Set the clf to the best combination of parameters
estimator = grid_obj.best_estimator_

# Fit the best algorithm to the data.
estimator.fit(X_train, y_train)

DecisionTreeClassifier(class_weight='balanced', max_depth=6, max_leaf_nodes=75,
                       min_samples_split=10, random_state=1)

DecisionTreeClassifier(class_weight='balanced', max_depth=6, max_leaf_nodes=75,
                       min_samples_split=10, random_state=1)

confusion_matrix_sklearn(model, X_train, y_train) ## create confusion matrix for train data

decision_tree_tune_perf_train = model_performance_classification_sklearn(model, X_train, y_train) ## check performance on train set
decision_tree_tune_perf_train

confusion_matrix_sklearn(model, X_test, y_test) ## create confusion matrix for test data

decision_tree_tune_perf_test = model_performance_classification_sklearn(model, X_test, y_test) ## Complete the code to check performance on test set
decision_tree_tune_perf_test

plt.figure(figsize=(20, 10))
out = tree.plot_tree(
    estimator,
    feature_names=feature_names,
    filled=True,
    fontsize=9,
    node_ids=False,
    class_names=None,
)
# below code will add arrows to the decision tree split if they are missing
for o in out:
    arrow = o.arrow_patch
    if arrow is not None:
        arrow.set_edgecolor("black")
        arrow.set_linewidth(1)
plt.show()

# Text report showing the rules of a decision tree -
print(tree.export_text(estimator, feature_names=feature_names, show_weights=True))

|--- lead_time <= 151.50
|   |--- no_of_special_requests <= 0.50
|   |   |--- market_segment_type_Online <= 0.50
|   |   |   |--- lead_time <= 90.50
|   |   |   |   |--- no_of_weekend_nights <= 0.50
|   |   |   |   |   |--- avg_price_per_room <= 196.50
|   |   |   |   |   |   |--- weights: [1736.39, 133.59] class: 0
|   |   |   |   |   |--- avg_price_per_room >  196.50
|   |   |   |   |   |   |--- weights: [0.75, 24.29] class: 1
|   |   |   |   |--- no_of_weekend_nights >  0.50
|   |   |   |   |   |--- lead_time <= 68.50
|   |   |   |   |   |   |--- weights: [960.27, 223.16] class: 0
|   |   |   |   |   |--- lead_time >  68.50
|   |   |   |   |   |   |--- weights: [129.73, 160.92] class: 1
|   |   |   |--- lead_time >  90.50
|   |   |   |   |--- lead_time <= 117.50
|   |   |   |   |   |--- avg_price_per_room <= 93.58
|   |   |   |   |   |   |--- weights: [214.72, 227.72] class: 1
|   |   |   |   |   |--- avg_price_per_room >  93.58
|   |   |   |   |   |   |--- weights: [82.76, 285.41] class: 1
|   |   |   |   |--- lead_time >  117.50
|   |   |   |   |   |--- no_of_week_nights <= 1.50
|   |   |   |   |   |   |--- weights: [87.23, 81.98] class: 0
|   |   |   |   |   |--- no_of_week_nights >  1.50
|   |   |   |   |   |   |--- weights: [228.14, 48.58] class: 0
|   |   |--- market_segment_type_Online >  0.50
|   |   |   |--- lead_time <= 13.50
|   |   |   |   |--- avg_price_per_room <= 99.44
|   |   |   |   |   |--- arrival_month <= 1.50
|   |   |   |   |   |   |--- weights: [92.45, 0.00] class: 0
|   |   |   |   |   |--- arrival_month >  1.50
|   |   |   |   |   |   |--- weights: [363.83, 132.08] class: 0
|   |   |   |   |--- avg_price_per_room >  99.44
|   |   |   |   |   |--- lead_time <= 3.50
|   |   |   |   |   |   |--- weights: [219.94, 85.01] class: 0
|   |   |   |   |   |--- lead_time >  3.50
|   |   |   |   |   |   |--- weights: [132.71, 280.85] class: 1
|   |   |   |--- lead_time >  13.50
|   |   |   |   |--- required_car_parking_space <= 0.50
|   |   |   |   |   |--- avg_price_per_room <= 71.92
|   |   |   |   |   |   |--- weights: [158.80, 159.40] class: 1
|   |   |   |   |   |--- avg_price_per_room >  71.92
|   |   |   |   |   |   |--- weights: [850.67, 3543.28] class: 1
|   |   |   |   |--- required_car_parking_space >  0.50
|   |   |   |   |   |--- no_of_weekend_nights <= 3.00
|   |   |   |   |   |   |--- weights: [48.46, 0.00] class: 0
|   |   |   |   |   |--- no_of_weekend_nights >  3.00
|   |   |   |   |   |   |--- weights: [0.00, 1.52] class: 1
|   |--- no_of_special_requests >  0.50
|   |   |--- no_of_special_requests <= 1.50
|   |   |   |--- market_segment_type_Online <= 0.50
|   |   |   |   |--- lead_time <= 102.50
|   |   |   |   |   |--- type_of_meal_plan_Not Selected <= 0.50
|   |   |   |   |   |   |--- weights: [697.09, 9.11] class: 0
|   |   |   |   |   |--- type_of_meal_plan_Not Selected >  0.50
|   |   |   |   |   |   |--- weights: [15.66, 9.11] class: 0
|   |   |   |   |--- lead_time >  102.50
|   |   |   |   |   |--- no_of_week_nights <= 2.50
|   |   |   |   |   |   |--- weights: [32.06, 19.74] class: 0
|   |   |   |   |   |--- no_of_week_nights >  2.50
|   |   |   |   |   |   |--- weights: [44.73, 3.04] class: 0
|   |   |   |--- market_segment_type_Online >  0.50
|   |   |   |   |--- lead_time <= 8.50
|   |   |   |   |   |--- lead_time <= 4.50
|   |   |   |   |   |   |--- weights: [498.03, 44.03] class: 0
|   |   |   |   |   |--- lead_time >  4.50
|   |   |   |   |   |   |--- weights: [258.71, 63.76] class: 0
|   |   |   |   |--- lead_time >  8.50
|   |   |   |   |   |--- required_car_parking_space <= 0.50
|   |   |   |   |   |   |--- weights: [2512.51, 1451.32] class: 0
|   |   |   |   |   |--- required_car_parking_space >  0.50
|   |   |   |   |   |   |--- weights: [134.20, 1.52] class: 0
|   |   |--- no_of_special_requests >  1.50
|   |   |   |--- lead_time <= 90.50
|   |   |   |   |--- no_of_week_nights <= 3.50
|   |   |   |   |   |--- weights: [1585.04, 0.00] class: 0
|   |   |   |   |--- no_of_week_nights >  3.50
|   |   |   |   |   |--- no_of_special_requests <= 2.50
|   |   |   |   |   |   |--- weights: [180.42, 57.69] class: 0
|   |   |   |   |   |--- no_of_special_requests >  2.50
|   |   |   |   |   |   |--- weights: [52.19, 0.00] class: 0
|   |   |   |--- lead_time >  90.50
|   |   |   |   |--- no_of_special_requests <= 2.50
|   |   |   |   |   |--- arrival_month <= 8.50
|   |   |   |   |   |   |--- weights: [184.90, 56.17] class: 0
|   |   |   |   |   |--- arrival_month >  8.50
|   |   |   |   |   |   |--- weights: [106.61, 106.27] class: 0
|   |   |   |   |--- no_of_special_requests >  2.50
|   |   |   |   |   |--- weights: [67.10, 0.00] class: 0
|--- lead_time >  151.50
|   |--- avg_price_per_room <= 100.04
|   |   |--- no_of_special_requests <= 0.50
|   |   |   |--- no_of_adults <= 1.50
|   |   |   |   |--- market_segment_type_Online <= 0.50
|   |   |   |   |   |--- lead_time <= 163.50
|   |   |   |   |   |   |--- weights: [3.73, 24.29] class: 1
|   |   |   |   |   |--- lead_time >  163.50
|   |   |   |   |   |   |--- weights: [257.96, 62.24] class: 0
|   |   |   |   |--- market_segment_type_Online >  0.50
|   |   |   |   |   |--- avg_price_per_room <= 2.50
|   |   |   |   |   |   |--- weights: [8.95, 3.04] class: 0
|   |   |   |   |   |--- avg_price_per_room >  2.50
|   |   |   |   |   |   |--- weights: [0.75, 97.16] class: 1
|   |   |   |--- no_of_adults >  1.50
|   |   |   |   |--- avg_price_per_room <= 82.47
|   |   |   |   |   |--- market_segment_type_Offline <= 0.50
|   |   |   |   |   |   |--- weights: [2.98, 282.37] class: 1
|   |   |   |   |   |--- market_segment_type_Offline >  0.50
|   |   |   |   |   |   |--- weights: [213.97, 385.60] class: 1
|   |   |   |   |--- avg_price_per_room >  82.47
|   |   |   |   |   |--- no_of_adults <= 2.50
|   |   |   |   |   |   |--- weights: [23.86, 1030.80] class: 1
|   |   |   |   |   |--- no_of_adults >  2.50
|   |   |   |   |   |   |--- weights: [5.22, 0.00] class: 0
|   |   |--- no_of_special_requests >  0.50
|   |   |   |--- no_of_weekend_nights <= 0.50
|   |   |   |   |--- lead_time <= 180.50
|   |   |   |   |   |--- lead_time <= 159.50
|   |   |   |   |   |   |--- weights: [7.46, 7.59] class: 1
|   |   |   |   |   |--- lead_time >  159.50
|   |   |   |   |   |   |--- weights: [37.28, 4.55] class: 0
|   |   |   |   |--- lead_time >  180.50
|   |   |   |   |   |--- no_of_special_requests <= 2.50
|   |   |   |   |   |   |--- weights: [20.13, 212.54] class: 1
|   |   |   |   |   |--- no_of_special_requests >  2.50
|   |   |   |   |   |   |--- weights: [8.95, 0.00] class: 0
|   |   |   |--- no_of_weekend_nights >  0.50
|   |   |   |   |--- market_segment_type_Offline <= 0.50
|   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |--- weights: [231.12, 110.82] class: 0
|   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |--- weights: [19.38, 34.92] class: 1
|   |   |   |   |--- market_segment_type_Offline >  0.50
|   |   |   |   |   |--- lead_time <= 348.50
|   |   |   |   |   |   |--- weights: [106.61, 3.04] class: 0
|   |   |   |   |   |--- lead_time >  348.50
|   |   |   |   |   |   |--- weights: [5.96, 4.55] class: 0
|   |--- avg_price_per_room >  100.04
|   |   |--- arrival_month <= 11.50
|   |   |   |--- no_of_special_requests <= 2.50
|   |   |   |   |--- market_segment_type_Offline <= 0.50
|   |   |   |   |   |--- weights: [0.00, 1835.40] class: 1
|   |   |   |   |--- market_segment_type_Offline >  0.50
|   |   |   |   |   |--- weights: [0.00, 1364.79] class: 1
|   |   |   |--- no_of_special_requests >  2.50
|   |   |   |   |--- weights: [23.11, 0.00] class: 0
|   |   |--- arrival_month >  11.50
|   |   |   |--- no_of_special_requests <= 0.50
|   |   |   |   |--- arrival_date <= 8.00
|   |   |   |   |   |--- weights: [0.75, 0.00] class: 0
|   |   |   |   |--- arrival_date >  8.00
|   |   |   |   |   |--- weights: [34.30, 0.00] class: 0
|   |   |   |--- no_of_special_requests >  0.50
|   |   |   |   |--- arrival_date <= 24.50
|   |   |   |   |   |--- weights: [3.73, 0.00] class: 0
|   |   |   |   |--- arrival_date >  24.50
|   |   |   |   |   |--- lead_time <= 172.50
|   |   |   |   |   |   |--- weights: [2.24, 1.52] class: 0
|   |   |   |   |   |--- lead_time >  172.50
|   |   |   |   |   |   |--- weights: [1.49, 21.25] class: 1

# importance of features in the tree building

importances = estimator.feature_importances_
indices = np.argsort(importances)

plt.figure(figsize=(8, 8))
plt.title("Feature Importances")
plt.barh(range(len(indices)), importances[indices], color="violet", align="center")
plt.yticks(range(len(indices)), [feature_names[i] for i in indices])
plt.xlabel("Relative Importance")
plt.show()

clf = DecisionTreeClassifier(random_state=1, class_weight="balanced")
path = clf.cost_complexity_pruning_path(X_train, y_train)
ccp_alphas, impurities = abs(path.ccp_alphas), path.impurities

pd.DataFrame(path)

fig, ax = plt.subplots(figsize=(10, 5))
ax.plot(ccp_alphas[:-1], impurities[:-1], marker="o", drawstyle="steps-post")
ax.set_xlabel("effective alpha")
ax.set_ylabel("total impurity of leaves")
ax.set_title("Total Impurity vs effective alpha for training set")
plt.show()

clfs = []
for ccp_alpha in ccp_alphas:
    clf = DecisionTreeClassifier(
        random_state=1, ccp_alpha=ccp_alpha, class_weight="balanced"
    )
    clf.fit(X_train,y_train) ## fit decision tree on training data
    clfs.append(clf)
print(
    "Number of nodes in the last tree is: {} with ccp_alpha: {}".format(
        clfs[-1].tree_.node_count, ccp_alphas[-1]
    )
)

Number of nodes in the last tree is: 1 with ccp_alpha: 0.08117914389137149

clfs = clfs[:-1]
ccp_alphas = ccp_alphas[:-1]

node_counts = [clf.tree_.node_count for clf in clfs]
depth = [clf.tree_.max_depth for clf in clfs]
fig, ax = plt.subplots(2, 1, figsize=(10, 7))
ax[0].plot(ccp_alphas, node_counts, marker="o", drawstyle="steps-post")
ax[0].set_xlabel("alpha")
ax[0].set_ylabel("number of nodes")
ax[0].set_title("Number of nodes vs alpha")
ax[1].plot(ccp_alphas, depth, marker="o", drawstyle="steps-post")
ax[1].set_xlabel("alpha")
ax[1].set_ylabel("depth of tree")
ax[1].set_title("Depth vs alpha")
fig.tight_layout()

clfs = []
for ccp_alpha in ccp_alphas:
    clf = DecisionTreeClassifier(
        random_state=1, ccp_alpha=ccp_alpha, class_weight="balanced"
    )
    clf.fit(X_test,y_test) ## fit decision tree on testing data
    clfs.append(clf)
print(
    "Number of nodes in the last tree is: {} with ccp_alpha: {}".format(
        clfs[-1].tree_.node_count, ccp_alphas[-1]
    )
)

Number of nodes in the last tree is: 7 with ccp_alpha: 0.03412090438134102

clfs = clfs[:-1]
ccp_alphas = ccp_alphas[:-1]

node_counts = [clf.tree_.node_count for clf in clfs]
depth = [clf.tree_.max_depth for clf in clfs]
fig, ax = plt.subplots(2, 1, figsize=(10, 7))
ax[0].plot(ccp_alphas, node_counts, marker="o", drawstyle="steps-post")
ax[0].set_xlabel("alpha")
ax[0].set_ylabel("number of nodes")
ax[0].set_title("Number of nodes vs alpha")
ax[1].plot(ccp_alphas, depth, marker="o", drawstyle="steps-post")
ax[1].set_xlabel("alpha")
ax[1].set_ylabel("depth of tree")
ax[1].set_title("Depth vs alpha")
fig.tight_layout()

f1_train = []
for clf in clfs:
    pred_train = clf.predict(X_train)
    values_train = f1_score(y_train, pred_train)
    f1_train.append(values_train)

f1_test = []
for clf in clfs:
    pred_test = clf.predict(X_test)
    values_test = f1_score(y_test, pred_test)
    f1_test.append(values_test)

fig, ax = plt.subplots(figsize=(15, 5))
ax.set_xlabel("alpha")
ax.set_ylabel("F1 Score")
ax.set_title("F1 Score vs alpha for training and testing sets")
ax.plot(ccp_alphas, f1_train, marker="o", label="train", drawstyle="steps-post")
ax.plot(ccp_alphas, f1_test, marker="o", label="test", drawstyle="steps-post")
ax.legend()
plt.show()

index_best_model = np.argmax(f1_test)
best_model = clfs[index_best_model]
print(best_model)

DecisionTreeClassifier(ccp_alpha=2.8537279759349284e-05,
                       class_weight='balanced', random_state=1)

confusion_matrix_sklearn(best_model, X_train, y_train)

decision_tree_post_perf_train = model_performance_classification_sklearn(
    best_model, X_train, y_train
)
decision_tree_post_perf_train

confusion_matrix_sklearn(best_model, X_test, y_test) ## create confusion matrix for test data on best model

decision_tree_post_perf_test = model_performance_classification_sklearn(
    best_model, X_test, y_test
) ## check performance of test set on best model
decision_tree_post_perf_test

plt.figure(figsize=(20, 10))

out = tree.plot_tree(
    best_model,
    feature_names=feature_names,
    filled=True,
    fontsize=9,
    node_ids=False,
    class_names=None,
)
for o in out:
    arrow = o.arrow_patch
    if arrow is not None:
        arrow.set_edgecolor("black")
        arrow.set_linewidth(1)
plt.show()

# Text report showing the rules of a decision tree -

print(tree.export_text(best_model, feature_names=feature_names, show_weights=True))

|--- lead_time <= 151.50
|   |--- no_of_special_requests <= 0.50
|   |   |--- market_segment_type_Online <= 0.50
|   |   |   |--- lead_time <= 74.50
|   |   |   |   |--- no_of_weekend_nights <= 0.50
|   |   |   |   |   |--- avg_price_per_room <= 164.93
|   |   |   |   |   |   |--- market_segment_type_Offline <= 0.50
|   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 <= 0.50
|   |   |   |   |   |   |   |   |--- lead_time <= 16.50
|   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 2.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 7.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  7.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
|   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  2.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 62.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  62.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |--- lead_time >  16.50
|   |   |   |   |   |   |   |   |   |--- arrival_date <= 18.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 20.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  20.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |--- arrival_date >  18.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 19.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  19.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 >  0.50
|   |   |   |   |   |   |   |   |--- no_of_adults <= 1.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 99.00
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 1.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.55] class: 1
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  1.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [9.61, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  99.00
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 1.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  1.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |--- no_of_adults >  1.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 1.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.48, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- lead_time >  1.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 10.82] class: 1
|   |   |   |   |   |   |--- market_segment_type_Offline >  0.50
|   |   |   |   |   |   |   |--- weights: [535.21, 0.00] class: 0
|   |   |   |   |   |--- avg_price_per_room >  164.93
|   |   |   |   |   |   |--- no_of_adults <= 2.50
|   |   |   |   |   |   |   |--- no_of_children <= 1.00
|   |   |   |   |   |   |   |   |--- weights: [0.00, 13.91] class: 1
|   |   |   |   |   |   |   |--- no_of_children >  1.00
|   |   |   |   |   |   |   |   |--- weights: [1.48, 0.00] class: 0
|   |   |   |   |   |   |--- no_of_adults >  2.50
|   |   |   |   |   |   |   |--- weights: [1.48, 0.00] class: 0
|   |   |   |   |--- no_of_weekend_nights >  0.50
|   |   |   |   |   |--- lead_time <= 65.50
|   |   |   |   |   |   |--- no_of_weekend_nights <= 4.00
|   |   |   |   |   |   |   |--- arrival_date <= 18.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 68.25
|   |   |   |   |   |   |   |   |   |--- weights: [82.79, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  68.25
|   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 15.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  15.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8
|   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 >  0.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 5.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  5.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
|   |   |   |   |   |   |   |--- arrival_date >  18.50
|   |   |   |   |   |   |   |   |--- arrival_month <= 3.50
|   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights <= 1.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 11.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [8.87, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  11.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
|   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights >  1.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 1.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [3.70, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  1.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |--- arrival_month >  3.50
|   |   |   |   |   |   |   |   |   |--- arrival_date <= 19.50
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
|   |   |   |   |   |   |   |   |   |--- arrival_date >  19.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 48.75
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  48.75
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 9
|   |   |   |   |   |   |--- no_of_weekend_nights >  4.00
|   |   |   |   |   |   |   |--- weights: [0.00, 9.27] class: 1
|   |   |   |   |   |--- lead_time >  65.50
|   |   |   |   |   |   |--- avg_price_per_room <= 73.62
|   |   |   |   |   |   |   |--- weights: [8.87, 0.00] class: 0
|   |   |   |   |   |   |--- avg_price_per_room >  73.62
|   |   |   |   |   |   |   |--- arrival_month <= 10.50
|   |   |   |   |   |   |   |   |--- no_of_week_nights <= 3.00
|   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights <= 1.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [2.96, 12.36] class: 1
|   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights >  1.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [2.22, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- no_of_week_nights >  3.00
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 27.81] class: 1
|   |   |   |   |   |   |   |--- arrival_month >  10.50
|   |   |   |   |   |   |   |   |--- weights: [2.22, 0.00] class: 0
|   |   |   |--- lead_time >  74.50
|   |   |   |   |--- arrival_month <= 10.50
|   |   |   |   |   |--- no_of_week_nights <= 0.50
|   |   |   |   |   |   |--- avg_price_per_room <= 92.50
|   |   |   |   |   |   |   |--- weights: [2.22, 1.55] class: 0
|   |   |   |   |   |   |--- avg_price_per_room >  92.50
|   |   |   |   |   |   |   |--- arrival_date <= 9.50
|   |   |   |   |   |   |   |   |--- arrival_date <= 8.00
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.55] class: 1
|   |   |   |   |   |   |   |   |--- arrival_date >  8.00
|   |   |   |   |   |   |   |   |   |--- weights: [1.48, 0.00] class: 0
|   |   |   |   |   |   |   |--- arrival_date >  9.50
|   |   |   |   |   |   |   |   |--- weights: [0.00, 43.26] class: 1
|   |   |   |   |   |--- no_of_week_nights >  0.50
|   |   |   |   |   |   |--- lead_time <= 117.50
|   |   |   |   |   |   |   |--- lead_time <= 92.50
|   |   |   |   |   |   |   |   |--- arrival_month <= 4.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 77.67
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 25.33
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 3.09] class: 1
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  25.33
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [8.87, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  77.67
|   |   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 1 <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [2.22, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 1 >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |--- arrival_month >  4.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 64.38
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.55] class: 1
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  64.38
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 100.76
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  100.76
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [22.18, 0.00] class: 0
|   |   |   |   |   |   |   |--- lead_time >  92.50
|   |   |   |   |   |   |   |   |--- arrival_month <= 3.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 73.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 2.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 18.54] class: 1
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  2.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [7.39, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  73.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [19.22, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- arrival_month >  3.50
|   |   |   |   |   |   |   |   |   |--- arrival_date <= 29.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 4.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  4.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 11
|   |   |   |   |   |   |   |   |   |--- arrival_date >  29.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 114.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  114.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.74, 0.00] class: 0
|   |   |   |   |   |   |--- lead_time >  117.50
|   |   |   |   |   |   |   |--- arrival_month <= 5.50
|   |   |   |   |   |   |   |   |--- arrival_date <= 27.50
|   |   |   |   |   |   |   |   |   |--- weights: [36.22, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- arrival_date >  27.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 85.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.55] class: 1
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  85.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [2.22, 0.00] class: 0
|   |   |   |   |   |   |   |--- arrival_month >  5.50
|   |   |   |   |   |   |   |   |--- no_of_adults <= 1.50
|   |   |   |   |   |   |   |   |   |--- weights: [25.13, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- no_of_adults >  1.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 89.88
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 77.88
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  77.88
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [8.13, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  89.88
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 7.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  7.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |--- arrival_month >  10.50
|   |   |   |   |   |--- no_of_weekend_nights <= 1.50
|   |   |   |   |   |   |--- weights: [41.40, 0.00] class: 0
|   |   |   |   |   |--- no_of_weekend_nights >  1.50
|   |   |   |   |   |   |--- lead_time <= 136.50
|   |   |   |   |   |   |   |--- weights: [8.87, 0.00] class: 0
|   |   |   |   |   |   |--- lead_time >  136.50
|   |   |   |   |   |   |   |--- weights: [0.00, 1.55] class: 1
|   |   |--- market_segment_type_Online >  0.50
|   |   |   |--- lead_time <= 7.50
|   |   |   |   |--- no_of_weekend_nights <= 1.50
|   |   |   |   |   |--- arrival_month <= 9.50
|   |   |   |   |   |   |--- lead_time <= 2.50
|   |   |   |   |   |   |   |--- avg_price_per_room <= 202.25
|   |   |   |   |   |   |   |   |--- no_of_weekend_nights <= 0.50
|   |   |   |   |   |   |   |   |   |--- arrival_year <= 2017.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 26.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  26.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |--- arrival_year >  2017.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 23.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  23.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
|   |   |   |   |   |   |   |   |--- no_of_weekend_nights >  0.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 126.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 10.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  10.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  126.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [8.13, 0.00] class: 0
|   |   |   |   |   |   |   |--- avg_price_per_room >  202.25
|   |   |   |   |   |   |   |   |--- weights: [0.00, 3.09] class: 1
|   |   |   |   |   |   |--- lead_time >  2.50
|   |   |   |   |   |   |   |--- avg_price_per_room <= 120.30
|   |   |   |   |   |   |   |   |--- lead_time <= 5.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 94.52
|   |   |   |   |   |   |   |   |   |   |--- weights: [15.52, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  94.52
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 16.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  16.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [5.17, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- lead_time >  5.50
|   |   |   |   |   |   |   |   |   |--- arrival_date <= 12.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [3.70, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- arrival_date >  12.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 6.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  6.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |--- avg_price_per_room >  120.30
|   |   |   |   |   |   |   |   |--- arrival_date <= 20.50
|   |   |   |   |   |   |   |   |   |--- arrival_date <= 1.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.74, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- arrival_date >  1.50
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.74, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |--- arrival_date >  20.50
|   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 1 <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [2.96, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 1 >  0.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_year <= 2017.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [2.22, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- arrival_year >  2017.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |--- arrival_month >  9.50
|   |   |   |   |   |   |--- arrival_date <= 25.50
|   |   |   |   |   |   |   |--- weights: [55.44, 0.00] class: 0
|   |   |   |   |   |   |--- arrival_date >  25.50
|   |   |   |   |   |   |   |--- arrival_date <= 27.00
|   |   |   |   |   |   |   |   |--- weights: [0.00, 1.55] class: 1
|   |   |   |   |   |   |   |--- arrival_date >  27.00
|   |   |   |   |   |   |   |   |--- weights: [9.61, 0.00] class: 0
|   |   |   |   |--- no_of_weekend_nights >  1.50
|   |   |   |   |   |--- arrival_year <= 2017.50
|   |   |   |   |   |   |--- no_of_week_nights <= 7.50
|   |   |   |   |   |   |   |--- weights: [13.31, 0.00] class: 0
|   |   |   |   |   |   |--- no_of_week_nights >  7.50
|   |   |   |   |   |   |   |--- weights: [0.00, 1.55] class: 1
|   |   |   |   |   |--- arrival_year >  2017.50
|   |   |   |   |   |   |--- arrival_month <= 1.50
|   |   |   |   |   |   |   |--- weights: [5.17, 0.00] class: 0
|   |   |   |   |   |   |--- arrival_month >  1.50
|   |   |   |   |   |   |   |--- no_of_week_nights <= 0.50
|   |   |   |   |   |   |   |   |--- weights: [3.70, 0.00] class: 0
|   |   |   |   |   |   |   |--- no_of_week_nights >  0.50
|   |   |   |   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |   |   |   |--- arrival_date <= 2.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.74, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- arrival_date >  2.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 2.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  2.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |   |   |   |--- weights: [2.22, 0.00] class: 0
|   |   |   |--- lead_time >  7.50
|   |   |   |   |--- required_car_parking_space <= 0.50
|   |   |   |   |   |--- avg_price_per_room <= 93.12
|   |   |   |   |   |   |--- lead_time <= 28.50
|   |   |   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |   |   |--- arrival_month <= 1.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 24.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [16.26, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- lead_time >  24.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.55] class: 1
|   |   |   |   |   |   |   |   |--- arrival_month >  1.50
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 4.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 22.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 10
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  22.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [2.96, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- arrival_month >  4.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 16.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  16.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
|   |   |   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |   |   |--- lead_time <= 26.50
|   |   |   |   |   |   |   |   |   |--- weights: [26.61, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- lead_time >  26.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.55] class: 1
|   |   |   |   |   |   |--- lead_time >  28.50
|   |   |   |   |   |   |   |--- avg_price_per_room <= 29.10
|   |   |   |   |   |   |   |   |--- weights: [8.87, 0.00] class: 0
|   |   |   |   |   |   |   |--- avg_price_per_room >  29.10
|   |   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 1 <= 0.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 63.67
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 47.97
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 4.64] class: 1
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  47.97
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  63.67
|   |   |   |   |   |   |   |   |   |   |--- no_of_children <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 15
|   |   |   |   |   |   |   |   |   |   |--- no_of_children >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.48, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 1 >  0.50
|   |   |   |   |   |   |   |   |   |--- no_of_adults <= 1.50
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 1.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  1.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 9
|   |   |   |   |   |   |   |   |   |--- no_of_adults >  1.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 74.94
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  74.94
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 14
|   |   |   |   |   |--- avg_price_per_room >  93.12
|   |   |   |   |   |   |--- arrival_year <= 2017.50
|   |   |   |   |   |   |   |--- lead_time <= 95.00
|   |   |   |   |   |   |   |   |--- arrival_month <= 8.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 12.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.74, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- lead_time >  12.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 4.64] class: 1
|   |   |   |   |   |   |   |   |--- arrival_month >  8.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 211.83
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 94.55
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  94.55
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  211.83
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.55] class: 1
|   |   |   |   |   |   |   |--- lead_time >  95.00
|   |   |   |   |   |   |   |   |--- weights: [0.00, 18.54] class: 1
|   |   |   |   |   |   |--- arrival_year >  2017.50
|   |   |   |   |   |   |   |--- arrival_month <= 10.50
|   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 5 <= 0.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 130.65
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 128.85
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 16
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  128.85
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  130.65
|   |   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 21
|   |   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 5 >  0.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 173.93
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 4.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [8.87, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  4.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.55] class: 1
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  173.93
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.55] class: 1
|   |   |   |   |   |   |   |--- arrival_month >  10.50
|   |   |   |   |   |   |   |   |--- lead_time <= 24.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 10.50
|   |   |   |   |   |   |   |   |   |   |--- no_of_children <= 1.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |   |--- no_of_children >  1.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.74, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- lead_time >  10.50
|   |   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 1 <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 1 >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [17.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- lead_time >  24.50
|   |   |   |   |   |   |   |   |   |--- arrival_date <= 25.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 115.68
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  115.68
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
|   |   |   |   |   |   |   |   |   |--- arrival_date >  25.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 18.54] class: 1
|   |   |   |   |--- required_car_parking_space >  0.50
|   |   |   |   |   |--- weights: [25.87, 0.00] class: 0
|   |--- no_of_special_requests >  0.50
|   |   |--- no_of_special_requests <= 1.50
|   |   |   |--- market_segment_type_Online <= 0.50
|   |   |   |   |--- no_of_weekend_nights <= 2.50
|   |   |   |   |   |--- lead_time <= 96.50
|   |   |   |   |   |   |--- type_of_meal_plan_Not Selected <= 0.50
|   |   |   |   |   |   |   |--- weights: [283.13, 0.00] class: 0
|   |   |   |   |   |   |--- type_of_meal_plan_Not Selected >  0.50
|   |   |   |   |   |   |   |--- lead_time <= 61.00
|   |   |   |   |   |   |   |   |--- weights: [4.44, 0.00] class: 0
|   |   |   |   |   |   |   |--- lead_time >  61.00
|   |   |   |   |   |   |   |   |--- arrival_month <= 6.00
|   |   |   |   |   |   |   |   |   |--- weights: [0.74, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- arrival_month >  6.00
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.55] class: 1
|   |   |   |   |   |--- lead_time >  96.50
|   |   |   |   |   |   |--- lead_time <= 108.00
|   |   |   |   |   |   |   |--- arrival_date <= 12.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 100.38
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 4.64] class: 1
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  100.38
|   |   |   |   |   |   |   |   |   |--- weights: [1.48, 0.00] class: 0
|   |   |   |   |   |   |   |--- arrival_date >  12.50
|   |   |   |   |   |   |   |   |--- weights: [8.13, 0.00] class: 0
|   |   |   |   |   |   |--- lead_time >  108.00
|   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 2 <= 0.50
|   |   |   |   |   |   |   |   |--- weights: [28.83, 0.00] class: 0
|   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 2 >  0.50
|   |   |   |   |   |   |   |   |--- lead_time <= 120.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 114.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.74, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- lead_time >  114.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.55] class: 1
|   |   |   |   |   |   |   |   |--- lead_time >  120.50
|   |   |   |   |   |   |   |   |   |--- weights: [8.13, 0.00] class: 0
|   |   |   |   |--- no_of_weekend_nights >  2.50
|   |   |   |   |   |--- weights: [0.00, 1.55] class: 1
|   |   |   |--- market_segment_type_Online >  0.50
|   |   |   |   |--- lead_time <= 6.50
|   |   |   |   |   |--- arrival_date <= 1.50
|   |   |   |   |   |   |--- arrival_month <= 7.00
|   |   |   |   |   |   |   |--- weights: [3.70, 0.00] class: 0
|   |   |   |   |   |   |--- arrival_month >  7.00
|   |   |   |   |   |   |   |--- avg_price_per_room <= 102.50
|   |   |   |   |   |   |   |   |--- weights: [1.48, 0.00] class: 0
|   |   |   |   |   |   |   |--- avg_price_per_room >  102.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 120.31
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 4.64] class: 1
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  120.31
|   |   |   |   |   |   |   |   |   |--- weights: [0.74, 0.00] class: 0
|   |   |   |   |   |--- arrival_date >  1.50
|   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 2 <= 0.50
|   |   |   |   |   |   |   |--- no_of_weekend_nights <= 3.50
|   |   |   |   |   |   |   |   |--- lead_time <= 4.50
|   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 8.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [42.14, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  8.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7
|   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights >  0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [73.18, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- lead_time >  4.50
|   |   |   |   |   |   |   |   |   |--- arrival_date <= 4.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 149.75
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  149.75
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.48, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- arrival_date >  4.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 122.72
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [26.61, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  122.72
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |--- no_of_weekend_nights >  3.50
|   |   |   |   |   |   |   |   |--- lead_time <= 3.00
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.55] class: 1
|   |   |   |   |   |   |   |   |--- lead_time >  3.00
|   |   |   |   |   |   |   |   |   |--- weights: [1.48, 0.00] class: 0
|   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 2 >  0.50
|   |   |   |   |   |   |   |--- no_of_week_nights <= 2.50
|   |   |   |   |   |   |   |   |--- weights: [1.48, 0.00] class: 0
|   |   |   |   |   |   |   |--- no_of_week_nights >  2.50
|   |   |   |   |   |   |   |   |--- weights: [0.00, 1.55] class: 1
|   |   |   |   |--- lead_time >  6.50
|   |   |   |   |   |--- no_of_weekend_nights <= 2.50
|   |   |   |   |   |   |--- avg_price_per_room <= 105.98
|   |   |   |   |   |   |   |--- lead_time <= 76.50
|   |   |   |   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 1.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [28.83, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  1.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 12
|   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected >  0.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 1.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [8.87, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  1.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 13
|   |   |   |   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |   |   |   |--- weights: [69.49, 0.00] class: 0
|   |   |   |   |   |   |   |--- lead_time >  76.50
|   |   |   |   |   |   |   |   |--- arrival_year <= 2017.50
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 7.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 27.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 17.00] class: 1
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  27.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [2.22, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- arrival_month >  7.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 91.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  91.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7
|   |   |   |   |   |   |   |   |--- arrival_year >  2017.50
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 10.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 7.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  7.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 15
|   |   |   |   |   |   |   |   |   |--- arrival_month >  10.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 26.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 11
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  26.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 4.64] class: 1
|   |   |   |   |   |   |--- avg_price_per_room >  105.98
|   |   |   |   |   |   |   |--- required_car_parking_space <= 0.50
|   |   |   |   |   |   |   |   |--- lead_time <= 27.50
|   |   |   |   |   |   |   |   |   |--- arrival_date <= 11.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 5.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  5.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |--- arrival_date >  11.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 135.05
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 9
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  135.05
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 12
|   |   |   |   |   |   |   |   |--- lead_time >  27.50
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 8.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 3.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  3.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 13
|   |   |   |   |   |   |   |   |   |--- arrival_month >  8.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 91.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 18
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  91.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7
|   |   |   |   |   |   |   |--- required_car_parking_space >  0.50
|   |   |   |   |   |   |   |   |--- lead_time <= 146.50
|   |   |   |   |   |   |   |   |   |--- weights: [43.61, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- lead_time >  146.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.55] class: 1
|   |   |   |   |   |--- no_of_weekend_nights >  2.50
|   |   |   |   |   |   |--- lead_time <= 107.00
|   |   |   |   |   |   |   |--- avg_price_per_room <= 65.03
|   |   |   |   |   |   |   |   |--- weights: [0.74, 0.00] class: 0
|   |   |   |   |   |   |   |--- avg_price_per_room >  65.03
|   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 <= 0.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 21.63] class: 1
|   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 >  0.50
|   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 6.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.74, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  6.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 4.64] class: 1
|   |   |   |   |   |   |--- lead_time >  107.00
|   |   |   |   |   |   |   |--- weights: [2.96, 0.00] class: 0
|   |   |--- no_of_special_requests >  1.50
|   |   |   |--- lead_time <= 89.50
|   |   |   |   |--- no_of_week_nights <= 3.50
|   |   |   |   |   |--- weights: [691.18, 0.00] class: 0
|   |   |   |   |--- no_of_week_nights >  3.50
|   |   |   |   |   |--- no_of_week_nights <= 9.50
|   |   |   |   |   |   |--- lead_time <= 25.50
|   |   |   |   |   |   |   |--- avg_price_per_room <= 126.15
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 76.65
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 10.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.48, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- arrival_month >  10.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.55] class: 1
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  76.65
|   |   |   |   |   |   |   |   |   |--- weights: [32.53, 0.00] class: 0
|   |   |   |   |   |   |   |--- avg_price_per_room >  126.15
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 134.37
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 8.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 4.64] class: 1
|   |   |   |   |   |   |   |   |   |--- arrival_month >  8.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.74, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  134.37
|   |   |   |   |   |   |   |   |   |--- weights: [8.87, 0.00] class: 0
|   |   |   |   |   |   |--- lead_time >  25.50
|   |   |   |   |   |   |   |--- lead_time <= 37.50
|   |   |   |   |   |   |   |   |--- arrival_date <= 21.00
|   |   |   |   |   |   |   |   |   |--- arrival_date <= 4.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 3.09] class: 1
|   |   |   |   |   |   |   |   |   |--- arrival_date >  4.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [6.65, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- arrival_date >  21.00
|   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 4.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 29.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 9.27] class: 1
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  29.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  4.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.48, 0.00] class: 0
|   |   |   |   |   |   |   |--- lead_time >  37.50
|   |   |   |   |   |   |   |   |--- lead_time <= 50.50
|   |   |   |   |   |   |   |   |   |--- weights: [12.57, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- lead_time >  50.50
|   |   |   |   |   |   |   |   |   |--- arrival_date <= 28.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 10.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  10.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [11.83, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- arrival_date >  28.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 80.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 4.64] class: 1
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  80.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.48, 0.00] class: 0
|   |   |   |   |   |--- no_of_week_nights >  9.50
|   |   |   |   |   |   |--- no_of_special_requests <= 2.50
|   |   |   |   |   |   |   |--- weights: [0.00, 4.64] class: 1
|   |   |   |   |   |   |--- no_of_special_requests >  2.50
|   |   |   |   |   |   |   |--- weights: [0.74, 0.00] class: 0
|   |   |   |--- lead_time >  89.50
|   |   |   |   |--- no_of_special_requests <= 2.50
|   |   |   |   |   |--- lead_time <= 93.50
|   |   |   |   |   |   |--- type_of_meal_plan_Not Selected <= 0.50
|   |   |   |   |   |   |   |--- avg_price_per_room <= 133.65
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 120.67
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 9.00
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 112.58
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  112.58
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.74, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- arrival_month >  9.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.48, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  120.67
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 7.73] class: 1
|   |   |   |   |   |   |   |--- avg_price_per_room >  133.65
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 160.72
|   |   |   |   |   |   |   |   |   |--- weights: [2.22, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  160.72
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.55] class: 1
|   |   |   |   |   |   |--- type_of_meal_plan_Not Selected >  0.50
|   |   |   |   |   |   |   |--- weights: [1.48, 0.00] class: 0
|   |   |   |   |   |--- lead_time >  93.50
|   |   |   |   |   |   |--- avg_price_per_room <= 202.14
|   |   |   |   |   |   |   |--- arrival_month <= 9.50
|   |   |   |   |   |   |   |   |--- arrival_year <= 2017.50
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 7.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 6.18] class: 1
|   |   |   |   |   |   |   |   |   |--- arrival_month >  7.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 20.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [3.70, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  20.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.55] class: 1
|   |   |   |   |   |   |   |   |--- arrival_year >  2017.50
|   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 6.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 26.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  26.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  6.50
|   |   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 1 <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 4.64] class: 1
|   |   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 1 >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.74, 0.00] class: 0
|   |   |   |   |   |   |   |--- arrival_month >  9.50
|   |   |   |   |   |   |   |   |--- no_of_week_nights <= 0.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 104.40
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 4.64] class: 1
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  104.40
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.74, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- no_of_week_nights >  0.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 146.00
|   |   |   |   |   |   |   |   |   |   |--- arrival_year <= 2017.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [7.39, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- arrival_year >  2017.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 13
|   |   |   |   |   |   |   |   |   |--- lead_time >  146.00
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 88.49
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 4.64] class: 1
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  88.49
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.74, 0.00] class: 0
|   |   |   |   |   |   |--- avg_price_per_room >  202.14
|   |   |   |   |   |   |   |--- weights: [0.00, 4.64] class: 1
|   |   |   |   |--- no_of_special_requests >  2.50
|   |   |   |   |   |--- weights: [25.87, 0.00] class: 0
|--- lead_time >  151.50
|   |--- avg_price_per_room <= 100.06
|   |   |--- no_of_special_requests <= 0.50
|   |   |   |--- no_of_adults <= 1.50
|   |   |   |   |--- market_segment_type_Online <= 0.50
|   |   |   |   |   |--- no_of_weekend_nights <= 1.50
|   |   |   |   |   |   |--- lead_time <= 278.00
|   |   |   |   |   |   |   |--- arrival_month <= 5.50
|   |   |   |   |   |   |   |   |--- arrival_date <= 7.00
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 3.09] class: 1
|   |   |   |   |   |   |   |   |--- arrival_date >  7.00
|   |   |   |   |   |   |   |   |   |--- weights: [3.70, 0.00] class: 0
|   |   |   |   |   |   |   |--- arrival_month >  5.50
|   |   |   |   |   |   |   |   |--- lead_time <= 173.00
|   |   |   |   |   |   |   |   |   |--- arrival_date <= 20.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [20.70, 3.09] class: 0
|   |   |   |   |   |   |   |   |   |--- arrival_date >  20.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 4.64] class: 1
|   |   |   |   |   |   |   |   |--- lead_time >  173.00
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 90.30
|   |   |   |   |   |   |   |   |   |   |--- weights: [27.35, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  90.30
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 90.80
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.55] class: 1
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  90.80
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [34.74, 1.55] class: 0
|   |   |   |   |   |   |--- lead_time >  278.00
|   |   |   |   |   |   |   |--- avg_price_per_room <= 98.33
|   |   |   |   |   |   |   |   |--- lead_time <= 340.50
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 6.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.74, 1.55] class: 1
|   |   |   |   |   |   |   |   |   |--- arrival_month >  6.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [14.78, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- lead_time >  340.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 85.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 3.09] class: 1
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  85.00
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 8.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.48, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  8.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [2.96, 3.09] class: 1
|   |   |   |   |   |   |   |--- avg_price_per_room >  98.33
|   |   |   |   |   |   |   |   |--- weights: [0.00, 7.73] class: 1
|   |   |   |   |   |--- no_of_weekend_nights >  1.50
|   |   |   |   |   |   |--- avg_price_per_room <= 79.51
|   |   |   |   |   |   |   |--- weights: [1.48, 0.00] class: 0
|   |   |   |   |   |   |--- avg_price_per_room >  79.51
|   |   |   |   |   |   |   |--- weights: [0.00, 15.45] class: 1
|   |   |   |   |--- market_segment_type_Online >  0.50
|   |   |   |   |   |--- avg_price_per_room <= 70.20
|   |   |   |   |   |   |--- no_of_week_nights <= 1.50
|   |   |   |   |   |   |   |--- weights: [1.48, 0.00] class: 0
|   |   |   |   |   |   |--- no_of_week_nights >  1.50
|   |   |   |   |   |   |   |--- weights: [0.00, 6.18] class: 1
|   |   |   |   |   |--- avg_price_per_room >  70.20
|   |   |   |   |   |   |--- weights: [0.00, 37.08] class: 1
|   |   |   |--- no_of_adults >  1.50
|   |   |   |   |--- market_segment_type_Online <= 0.50
|   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |--- avg_price_per_room <= 84.62
|   |   |   |   |   |   |   |--- lead_time <= 234.50
|   |   |   |   |   |   |   |   |--- lead_time <= 194.50
|   |   |   |   |   |   |   |   |   |--- arrival_year <= 2017.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 168.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [3.70, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  168.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 21.63] class: 1
|   |   |   |   |   |   |   |   |   |--- arrival_year >  2017.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 5.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  5.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
|   |   |   |   |   |   |   |   |--- lead_time >  194.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 79.12
|   |   |   |   |   |   |   |   |   |   |--- weights: [17.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  79.12
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 80.49
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.55] class: 1
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  80.49
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [2.22, 0.00] class: 0
|   |   |   |   |   |   |   |--- lead_time >  234.50
|   |   |   |   |   |   |   |   |--- arrival_year <= 2017.50
|   |   |   |   |   |   |   |   |   |--- weights: [12.57, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- arrival_year >  2017.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 79.00
|   |   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 2 <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 2 >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.74, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  79.00
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 296.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [9.61, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  296.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 10.82] class: 1
|   |   |   |   |   |   |--- avg_price_per_room >  84.62
|   |   |   |   |   |   |   |--- no_of_weekend_nights <= 1.50
|   |   |   |   |   |   |   |   |--- arrival_date <= 2.00
|   |   |   |   |   |   |   |   |   |--- weights: [1.48, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- arrival_date >  2.00
|   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 321.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  321.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 >  0.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 17.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.55] class: 1
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  17.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.48, 0.00] class: 0
|   |   |   |   |   |   |   |--- no_of_weekend_nights >  1.50
|   |   |   |   |   |   |   |   |--- arrival_month <= 6.00
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 7.73] class: 1
|   |   |   |   |   |   |   |   |--- arrival_month >  6.00
|   |   |   |   |   |   |   |   |   |--- weights: [5.17, 0.00] class: 0
|   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |--- weights: [17.00, 0.00] class: 0
|   |   |   |   |--- market_segment_type_Online >  0.50
|   |   |   |   |   |--- no_of_adults <= 2.50
|   |   |   |   |   |   |--- avg_price_per_room <= 62.68
|   |   |   |   |   |   |   |--- avg_price_per_room <= 61.82
|   |   |   |   |   |   |   |   |--- weights: [0.00, 10.82] class: 1
|   |   |   |   |   |   |   |--- avg_price_per_room >  61.82
|   |   |   |   |   |   |   |   |--- weights: [0.74, 0.00] class: 0
|   |   |   |   |   |   |--- avg_price_per_room >  62.68
|   |   |   |   |   |   |   |--- arrival_date <= 3.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 72.79
|   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.74, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected >  0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.55] class: 1
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  72.79
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 47.90] class: 1
|   |   |   |   |   |   |   |--- arrival_date >  3.50
|   |   |   |   |   |   |   |   |--- weights: [0.00, 322.91] class: 1
|   |   |   |   |   |--- no_of_adults >  2.50
|   |   |   |   |   |   |--- weights: [0.74, 0.00] class: 0
|   |   |--- no_of_special_requests >  0.50
|   |   |   |--- market_segment_type_Online <= 0.50
|   |   |   |   |--- weights: [65.05, 0.00] class: 0
|   |   |   |--- market_segment_type_Online >  0.50
|   |   |   |   |--- lead_time <= 181.50
|   |   |   |   |   |--- avg_price_per_room <= 80.42
|   |   |   |   |   |   |--- weights: [16.26, 0.00] class: 0
|   |   |   |   |   |--- avg_price_per_room >  80.42
|   |   |   |   |   |   |--- arrival_month <= 10.50
|   |   |   |   |   |   |   |--- avg_price_per_room <= 92.52
|   |   |   |   |   |   |   |   |--- no_of_week_nights <= 5.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 85.25
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 13.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [4.44, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  13.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  85.25
|   |   |   |   |   |   |   |   |   |   |--- weights: [19.22, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- no_of_week_nights >  5.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.55] class: 1
|   |   |   |   |   |   |   |--- avg_price_per_room >  92.52
|   |   |   |   |   |   |   |   |--- lead_time <= 173.50
|   |   |   |   |   |   |   |   |   |--- arrival_date <= 18.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 98.38
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  98.38
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |--- arrival_date >  18.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [2.96, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- lead_time >  173.50
|   |   |   |   |   |   |   |   |   |--- weights: [5.17, 0.00] class: 0
|   |   |   |   |   |   |--- arrival_month >  10.50
|   |   |   |   |   |   |   |--- weights: [0.74, 4.64] class: 1
|   |   |   |   |--- lead_time >  181.50
|   |   |   |   |   |--- no_of_weekend_nights <= 0.50
|   |   |   |   |   |   |--- avg_price_per_room <= 77.40
|   |   |   |   |   |   |   |--- no_of_week_nights <= 4.50
|   |   |   |   |   |   |   |   |--- weights: [5.17, 0.00] class: 0
|   |   |   |   |   |   |   |--- no_of_week_nights >  4.50
|   |   |   |   |   |   |   |   |--- arrival_date <= 19.00
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.55] class: 1
|   |   |   |   |   |   |   |   |--- arrival_date >  19.00
|   |   |   |   |   |   |   |   |   |--- weights: [1.48, 0.00] class: 0
|   |   |   |   |   |   |--- avg_price_per_room >  77.40
|   |   |   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |   |   |--- required_car_parking_space <= 0.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 58.71] class: 1
|   |   |   |   |   |   |   |   |--- required_car_parking_space >  0.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.74, 0.00] class: 0
|   |   |   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 86.38
|   |   |   |   |   |   |   |   |   |--- weights: [2.22, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  86.38
|   |   |   |   |   |   |   |   |   |--- lead_time <= 238.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.74, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- lead_time >  238.00
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 91.05
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  91.05
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 4.64] class: 1
|   |   |   |   |   |--- no_of_weekend_nights >  0.50
|   |   |   |   |   |   |--- arrival_month <= 10.50
|   |   |   |   |   |   |   |--- avg_price_per_room <= 80.06
|   |   |   |   |   |   |   |   |--- weights: [16.26, 0.00] class: 0
|   |   |   |   |   |   |   |--- avg_price_per_room >  80.06
|   |   |   |   |   |   |   |   |--- no_of_week_nights <= 3.50
|   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 1 <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 1.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  1.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 1 >  0.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 22.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  22.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [12.57, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- no_of_week_nights >  3.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 194.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 9.27] class: 1
|   |   |   |   |   |   |   |   |   |--- lead_time >  194.00
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 222.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  222.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |--- arrival_month >  10.50
|   |   |   |   |   |   |   |--- lead_time <= 288.50
|   |   |   |   |   |   |   |   |--- no_of_children <= 0.50
|   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights <= 1.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 81.30
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  81.30
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [2.22, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights >  1.50
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 2.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 9.27] class: 1
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  2.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |--- no_of_children >  0.50
|   |   |   |   |   |   |   |   |   |--- weights: [2.22, 0.00] class: 0
|   |   |   |   |   |   |   |--- lead_time >  288.50
|   |   |   |   |   |   |   |   |--- weights: [2.96, 0.00] class: 0
|   |--- avg_price_per_room >  100.06
|   |   |--- arrival_month <= 11.50
|   |   |   |--- no_of_special_requests <= 2.50
|   |   |   |   |--- weights: [0.00, 1388.96] class: 1
|   |   |   |--- no_of_special_requests >  2.50
|   |   |   |   |--- weights: [12.57, 0.00] class: 0
|   |   |--- arrival_month >  11.50
|   |   |   |--- no_of_special_requests <= 0.50
|   |   |   |   |--- weights: [24.39, 0.00] class: 0
|   |   |   |--- no_of_special_requests >  0.50
|   |   |   |   |--- room_type_reserved_Room_Type 1 <= 0.50
|   |   |   |   |   |--- arrival_date <= 5.50
|   |   |   |   |   |   |--- weights: [0.74, 0.00] class: 0
|   |   |   |   |   |--- arrival_date >  5.50
|   |   |   |   |   |   |--- arrival_date <= 28.50
|   |   |   |   |   |   |   |--- weights: [0.00, 10.82] class: 1
|   |   |   |   |   |   |--- arrival_date >  28.50
|   |   |   |   |   |   |   |--- avg_price_per_room <= 103.92
|   |   |   |   |   |   |   |   |--- weights: [0.74, 0.00] class: 0
|   |   |   |   |   |   |   |--- avg_price_per_room >  103.92
|   |   |   |   |   |   |   |   |--- weights: [0.00, 1.55] class: 1
|   |   |   |   |--- room_type_reserved_Room_Type 1 >  0.50
|   |   |   |   |   |--- weights: [2.96, 0.00] class: 0

importances = best_model.feature_importances_
indices = np.argsort(importances)

plt.figure(figsize=(12, 12))
plt.title("Feature Importances")
plt.barh(range(len(indices)), importances[indices], color="violet", align="center")
plt.yticks(range(len(indices)), [feature_names[i] for i in indices])
plt.xlabel("Relative Importance")
plt.show()

# training performance comparison

models_train_comp_df = pd.concat(
    [
        decision_tree_perf_train.T,
        decision_tree_tune_perf_train.T,
        decision_tree_post_perf_train.T,
    ],
    axis=1,
)
models_train_comp_df.columns = [
    "Decision Tree sklearn",
    "Decision Tree (Pre-Pruning)",
    "Decision Tree (Post-Pruning)",
]
print("Training performance comparison:")
models_train_comp_df

Training performance comparison:

# testing performance comparison

models_test_comp_df = pd.concat(
    [
        decision_tree_perf_test.T,
        decision_tree_tune_perf_test.T,
        decision_tree_post_perf_test.T,
    ],
    axis=1,
)
models_test_comp_df.columns = [
    "Decision Tree sklearn",
    "Decision Tree (Pre-Pruning)",
    "Decision Tree (Post-Pruning)",
]
print("Test performance comparison:")
models_test_comp_df## Complete the code to compare performance of test set

Test performance comparison:

	count	unique	top	freq	mean	std	min	25%	50%	75%	max
no_of_adults	36275.00000	NaN	NaN	NaN	1.84496	0.51871	0.00000	2.00000	2.00000	2.00000	4.00000
no_of_children	36275.00000	NaN	NaN	NaN	0.10528	0.40265	0.00000	0.00000	0.00000	0.00000	10.00000
no_of_weekend_nights	36275.00000	NaN	NaN	NaN	0.81072	0.87064	0.00000	0.00000	1.00000	2.00000	7.00000
no_of_week_nights	36275.00000	NaN	NaN	NaN	2.20430	1.41090	0.00000	1.00000	2.00000	3.00000	17.00000
type_of_meal_plan	36275	4	Meal Plan 1	27835	NaN	NaN	NaN	NaN	NaN	NaN	NaN
required_car_parking_space	36275.00000	NaN	NaN	NaN	0.03099	0.17328	0.00000	0.00000	0.00000	0.00000	1.00000
room_type_reserved	36275	7	Room_Type 1	28130	NaN	NaN	NaN	NaN	NaN	NaN	NaN
lead_time	36275.00000	NaN	NaN	NaN	85.23256	85.93082	0.00000	17.00000	57.00000	126.00000	443.00000
arrival_year	36275.00000	NaN	NaN	NaN	2017.82043	0.38384	2017.00000	2018.00000	2018.00000	2018.00000	2018.00000
arrival_month	36275.00000	NaN	NaN	NaN	7.42365	3.06989	1.00000	5.00000	8.00000	10.00000	12.00000
arrival_date	36275.00000	NaN	NaN	NaN	15.59700	8.74045	1.00000	8.00000	16.00000	23.00000	31.00000
market_segment_type	36275	5	Online	23214	NaN	NaN	NaN	NaN	NaN	NaN	NaN
repeated_guest	36275.00000	NaN	NaN	NaN	0.02564	0.15805	0.00000	0.00000	0.00000	0.00000	1.00000
no_of_previous_cancellations	36275.00000	NaN	NaN	NaN	0.02335	0.36833	0.00000	0.00000	0.00000	0.00000	13.00000
no_of_previous_bookings_not_canceled	36275.00000	NaN	NaN	NaN	0.15341	1.75417	0.00000	0.00000	0.00000	0.00000	58.00000
avg_price_per_room	36275.00000	NaN	NaN	NaN	103.42354	35.08942	0.00000	80.30000	99.45000	120.00000	540.00000
no_of_special_requests	36275.00000	NaN	NaN	NaN	0.61966	0.78624	0.00000	0.00000	0.00000	1.00000	5.00000
booking_status	36275	2	Not_Canceled	24390	NaN	NaN	NaN	NaN	NaN	NaN	NaN

	const	no_of_weekend_nights	no_of_week_nights	required_car_parking_space	lead_time	arrival_year	arrival_month	repeated_guest	no_of_previous_cancellations	avg_price_per_room	no_of_special_requests	type_of_meal_plan_Meal Plan 2	type_of_meal_plan_Not Selected	room_type_reserved_Room_Type 2	room_type_reserved_Room_Type 4	room_type_reserved_Room_Type 5	room_type_reserved_Room_Type 6	room_type_reserved_Room_Type 7	market_segment_type_Corporate	market_segment_type_Offline
Odds	0.00000	1.16153	1.03682	0.19886	1.01601	1.53653	0.95191	0.04623	1.33523	1.01925	0.22638	1.18026	1.23366	0.68845	0.76451	0.50213	0.47521	0.26776	0.41854	0.17012
Change_odd%	-100.00000	16.15325	3.68173	-80.11371	1.60134	53.65324	-4.80895	-95.37690	33.52334	1.92531	-77.36171	18.02564	23.36580	-31.15506	-23.54886	-49.78672	-52.47870	-73.22410	-58.14571	-82.98809

	Logistic Regression-default Threshold	Logistic Regression-0.37 Threshold	Logistic Regression-0.42 Threshold
Accuracy	0.80663	0.78430	0.80265
Recall	0.63265	0.76608	0.69876
Precision	0.73950	0.64348	0.69885
F1	0.68191	0.69945	0.69880

	Logistic Regression-default Threshold	Logistic Regression-0.37 Threshold	Logistic Regression-0.42 Threshold
Accuracy	0.80272	0.77846	0.79656
Recall	0.62619	0.76612	0.69405
Precision	0.73285	0.63402	0.68788
F1	0.67534	0.69384	0.69095

	ccp_alphas	impurities
0	0.00000	0.00838
1	0.00000	0.00838
2	0.00000	0.00838
3	0.00000	0.00838
4	0.00000	0.00838
...	...	...
1839	0.00890	0.32806
1840	0.00980	0.33786
1841	0.01272	0.35058
1842	0.03412	0.41882
1843	0.08118	0.50000

INN Hotels Project¶

Context¶

Objective¶

Data Description¶

Importing necessary libraries and data¶

Exploratory Data Analysis (EDA)¶

Data Preprocessing¶

	Booking_ID	no_of_adults	no_of_weekend_nights	no_of_week_nights	type_of_meal_plan	room_type_reserved	lead_time	arrival_year	arrival_month	arrival_date	market_segment_type	avg_price_per_room	no_of_special_requests	booking_status
0	INN00001	2	1	2	Meal Plan 1	Room_Type 1	224	2017	10	2	Offline	65.00000	0	Not_Canceled
1	INN00002	2	2	3	Not Selected	Room_Type 1	5	2018	11	6	Online	106.68000	1	Not_Canceled
2	INN00003	1	2	1	Meal Plan 1	Room_Type 1	1	2018	2	28	Online	60.00000	0	Canceled
3	INN00004	2	0	2	Meal Plan 1	Room_Type 1	211	2018	5	20	Online	100.00000	0	Canceled
4	INN00005	2	1	1	Not Selected	Room_Type 1	48	2018	4	11	Online	94.50000	0	Canceled

	Booking_ID	no_of_adults	no_of_weekend_nights	no_of_week_nights	type_of_meal_plan	room_type_reserved	lead_time	arrival_year	arrival_month	arrival_date	market_segment_type	avg_price_per_room	no_of_special_requests	booking_status
36270	INN36271	3	2	6	Meal Plan 1	Room_Type 4	85	2018	8	3	Online	167.80000	1	Not_Canceled
36271	INN36272	2	1	3	Meal Plan 1	Room_Type 1	228	2018	10	17	Online	90.95000	2	Canceled
36272	INN36273	2	2	6	Meal Plan 1	Room_Type 1	148	2018	7	1	Online	98.39000	2	Not_Canceled
36273	INN36274	2	0	3	Not Selected	Room_Type 1	63	2018	4	21	Online	94.50000	0	Canceled
36274	INN36275	2	1	2	Meal Plan 1	Room_Type 1	207	2018	12	30	Offline	161.67000	0	Not_Canceled

	no_of_adults	no_of_children	no_of_weekend_nights	no_of_week_nights	required_car_parking_space	lead_time	arrival_year	arrival_month	arrival_date	repeated_guest	no_of_previous_cancellations	no_of_previous_bookings_not_canceled	avg_price_per_room	no_of_special_requests	booking_status
count	10275.00000	10275.00000	10275.00000	10275.00000	10275.00000	10275.00000	10275.00000	10275.00000	10275.00000	10275.00000	10275.00000	10275.00000	10275.00000	10275.00000	10275.00000
mean	1.73100	0.01431	0.62939	2.05849	0.00282	132.75299	2017.74491	7.65324	15.20925	0.00759	0.01022	0.00234	97.67368	0.31017	0.43241
std	0.47381	0.13984	0.79862	1.10037	0.05305	104.92263	0.43593	2.87524	8.44651	0.08680	0.23594	0.08710	25.89633	0.60628	0.49543
min	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	2017.00000	1.00000	1.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000
25%	1.00000	0.00000	0.00000	1.00000	0.00000	41.00000	2017.00000	6.00000	8.00000	0.00000	0.00000	0.00000	80.00000	0.00000	0.00000
50%	2.00000	0.00000	0.00000	2.00000	0.00000	105.00000	2018.00000	8.00000	15.00000	0.00000	0.00000	0.00000	96.30000	0.00000	0.00000
75%	2.00000	0.00000	1.00000	3.00000	0.00000	208.00000	2018.00000	10.00000	22.00000	0.00000	0.00000	0.00000	114.00000	0.00000	1.00000
max	3.00000	3.00000	5.00000	15.00000	1.00000	443.00000	2018.00000	12.00000	31.00000	1.00000	13.00000	6.00000	285.17000	5.00000	1.00000

	no_of_adults	no_of_children	no_of_weekend_nights	no_of_week_nights	required_car_parking_space	lead_time	arrival_year	arrival_month	arrival_date	repeated_guest	no_of_previous_cancellations	no_of_previous_bookings_not_canceled	avg_price_per_room	no_of_special_requests	booking_status
count	26000.00000	26000.00000	26000.00000	26000.00000	26000.00000	26000.00000	26000.00000	26000.00000	26000.00000	26000.00000	26000.00000	26000.00000	26000.00000	26000.00000	26000.00000
mean	1.89000	0.14050	0.88238	2.26192	0.04212	66.45285	2017.85027	7.33292	15.75023	0.03277	0.02854	0.21312	105.68198	0.74196	0.28623
std	0.52870	0.45285	0.88737	1.51234	0.20086	68.60680	0.35681	3.13892	8.84939	0.17804	0.40889	2.06824	37.78347	0.81511	0.45201
min	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	2017.00000	1.00000	1.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000
25%	2.00000	0.00000	0.00000	1.00000	0.00000	12.00000	2018.00000	5.00000	8.00000	0.00000	0.00000	0.00000	80.75000	0.00000	0.00000
50%	2.00000	0.00000	1.00000	2.00000	0.00000	44.00000	2018.00000	8.00000	16.00000	0.00000	0.00000	0.00000	100.00000	1.00000	0.00000
75%	2.00000	0.00000	2.00000	3.00000	0.00000	100.00000	2018.00000	10.00000	23.00000	0.00000	0.00000	0.00000	127.00000	1.00000	1.00000
max	4.00000	3.00000	7.00000	17.00000	1.00000	443.00000	2018.00000	12.00000	31.00000	1.00000	13.00000	58.00000	375.50000	5.00000	1.00000

	feature	VIF
0	const	39624634.31385
1	no_of_adults	1.34705
2	no_of_children	2.08820
3	no_of_weekend_nights	1.06732
4	no_of_week_nights	1.09435
5	required_car_parking_space	1.03499
6	lead_time	1.40219
7	arrival_year	1.43432
8	arrival_month	1.27809
9	arrival_date	1.00771
10	repeated_guest	1.75027
11	no_of_previous_cancellations	1.32201
12	no_of_previous_bookings_not_canceled	1.57089
13	avg_price_per_room	2.05023
14	no_of_special_requests	1.24765
15	type_of_meal_plan_Meal Plan 2	1.26322
16	type_of_meal_plan_Meal Plan 3	1.00797
17	type_of_meal_plan_Not Selected	1.27995
18	room_type_reserved_Room_Type 2	1.09653
19	room_type_reserved_Room_Type 3	1.00390
20	room_type_reserved_Room_Type 4	1.35779
21	room_type_reserved_Room_Type 5	1.03107
22	room_type_reserved_Room_Type 6	2.04832
23	room_type_reserved_Room_Type 7	1.09399
24	market_segment_type_Complementary	4.35263
25	market_segment_type_Corporate	16.63449
26	market_segment_type_Offline	62.51418
27	market_segment_type_Online	69.47432

	Decision Tree sklearn	Decision Tree (Pre-Pruning)	Decision Tree (Post-Pruning)
Accuracy	0.99421	0.99421	0.84735
Recall	0.98661	0.98661	0.76719
Precision	0.99578	0.99578	0.76884
F1	0.99117	0.99117	0.76802

	Decision Tree sklearn	Decision Tree (Pre-Pruning)	Decision Tree (Post-Pruning)
Accuracy	0.87182	0.87182	0.99568
Recall	0.80522	0.80522	0.99744
Precision	0.80000	0.80000	0.98930
F1	0.80260	0.80260	0.99336